#computer aided drug design

[drugdesignerlife]

CADD Vault

It's an open-source repository dedicated to sharing resources, tools, and knowledge in the field of computer-aided drug design.

You can look here for some resources if you wish.

You can find here websites and github repositories.

[angeltheirs]

Really been thinking about buying a church or something with what money? so that I can renovate it into a shelter for unhoused people with minimal screening. Got stuff? Okay, you can keep it. Animals? Anyone with an animal gets a crate for it to keep near their bed for everyone's and every animal's safety and security. Drugs? Okay, there's a designated room for them. I'd love to have the funds to hire counselors if people were interested. I'll get a safe haven license IDK how that works, this is a fantasy of what I'd do if I won the lottery A computer room where people can apply for jobs/aid or what's left of it and housing later on after they get a job and a place where they can get mail delivered. There's a mail call after dinner. Three square meals a day, and snacks. Laundry on site. There's more, I know, hidden up there that I was thinking about but I can't recall it at this present moment

[studyinmoon]

15 days of productivity

Day 4

Completed and submitted the assignment for week 5 of the drug designing course I'm taking currently. Today as I was solving questions I realised this is not only helping me to understand computer aided drug design process better but also greatly helping me better my calculus skills which I had lost along the 3 years I had no application for it in my bachelor's. I am finally gaining some confidence that I can perform well in the certification exam which is less than a month away.

I supposed to listen to the first 45 minutes of an 8 hour lecture on isomerism but due to time constraints I couldn't and now I'm too sleepy.

In other news, today I formally joined as a content writer intern with Pehchaan- A Street School which is an NGO dedicated to providing quality education to children who cannot afford to go to school. We had an induction meet which was supposed to be an hour but ended up being 2 hours due to which I wasn't able to give time to chemistry.

I could've done better but considering that I've still not gotten my potential adhd assessed (and don't have any access to medicine) I did pretty well. Only took a 4.5 hour break while working on my assignment today which were taken in small amounts of 35-45 minutes for meals and rest. And that is a massive improvement. Hopefully I keep it up tomorrow and so on cause I have an extremely important entrance exam for master's in December which I haven't really started preparing for in earnest. Fingers crossed.

Thanks for reading. Kindly follow for more adhd ramblings and if you want to keep up with my chaotic academia.

[wynjournal]

Funny/Out Of Context Ross Scott Quotes

(I might have to make a part 2 to this but I'm tired of having this in my drafts.)

"Uh, yeah, actually I DO expect you to believe he just stepped out in front of my headlights. This motherfucker popped out of a manhole to get run over by me. Why don't you go for a drive around the city before casting judgment on who I run over, huh?!"

"The hovercars work on an electric grid like trams. This limits where you can drive. It's like DRM for your car. I can't wait for the future!"

"All he wanted was a vial of my blood, and the exact time of my birth. He paid for shipping any everything. Can't argue with that!'

"Okay. Here's the problem with the sun. It was designed by maniacs. That's the problem."

"I have nothing against you if you're from LA. I just hate your city. In fact, if you live in LA, good for you. You're living out there so the rest of us don't have to. You're taking one for the team."

"We need to summon the mothership and it's not going to happen if people think we're the Mickey Mouse Club."

"I sabotaged the population's food supply, brought about a transportation crisis and committed environmental terrorism. I fabricated evidence against the regime, extorted people to broadcast it, aided and funded rebels of the current regime, completely used and manipulated people who cared about me and put their trust in me, outright betrayed and killed one of them. Half the words coming out of my mouth were lies. I convinced the indigenous people to work against their own interest and give up valuable resources to further our agenda. I bribed guards, killed cops, staged a prison break of public enemies and to top it all off, called in an orbital strike on the entire city while I got the hell out of dodge. Wow! Just about the only thing I didn't do was work with cartels to bring illegal drugs in to ruin communities and according to the general, that might be because the current regime already beat us to it. There's a certain unspoken callousness to it. Also I sometimes killed civilians by accident and there were never any consequences to it. They were just collateral damage. The vibe I got was "You're here to bring down society people are just props. Keep your eyes on the prize.""

"'Games as a service is fraud'."

"First you race him, but then he cheats so you hold a gun to his head, then you race him again, but then he cheats again, so then you finally race him."

"The secret was to do the exact same thing I already did, with no changes at all, with no understanding why it worked this time and not earlier. Computers!"

"I am not getting the lowest grade this time. Oh no! I am getting... THE SECOND LOWEST GRADE! Yeah! This is practically a metaphor. it shows if you work really hard, you get ahead in life."

[innonurse]

AI-designed cancer drug blocks tumor growth without common side effects

- By InnoNurse Staff -

A new cancer drug candidate, BBO-10203, developed by Lawrence Livermore National Laboratory (LLNL), BridgeBio Oncology Therapeutics (BBOT), and the Frederick National Laboratory for Cancer Research (FNLCR), has shown promise in blocking tumor growth without triggering hyperglycemia—a common side effect in similar therapies.

What sets this drug apart is the computational-first approach used in its development. LLNL leveraged its Livermore Computer-Aided Drug Design (LCADD) platform, which integrates artificial intelligence (AI), machine learning (ML), and physics-based modeling with the power of DOE supercomputers like Ruby and Lassen.

This allowed researchers to simulate and predict how millions of potential drug molecules would interact with cancer-related proteins—before any compound was physically created. This approach drastically reduced the cost, time, and failure rate typically associated with drug discovery.

Specifically, BBO-10203 targets a difficult-to-drug interaction between two proteins—RAS and PI3Kα—that are frequently mutated in cancer. Using AI and ML, the team analyzed structural biology data, refined molecular designs, and selected optimal candidates through iterative simulations. Crystallography and lab testing then validated the best designs, leading to a molecule with high selectivity, potency, and fewer side effects.

Currently in Phase 1 clinical trials for breast, lung, and colorectal cancers, BBO-10203 is part of a broader effort to transform cancer treatment through AI-driven drug discovery.

The project demonstrates how computational modeling, supercomputing, and biology can converge to create safer, more effective therapies at unprecedented speed.

Header image credit: LLNL.

Read more at LLNL

[agi-institutions]

Bachelor of Pharmacy in Lucknow – A Step Towards a Promising Pharmaceutical Career

Are you passionate about medicines, healthcare, and drug discovery? Are you aiming to become a skilled pharmacist, research expert, or medical professional? If yes, then choosing a Bachelor of Pharmacy in Lucknow could be the perfect decision for your career in the pharmaceutical sciences.

Lucknow, the capital of Uttar Pradesh, has evolved into a hub for quality higher education in the field of pharmacy. The city offers state-of-the-art infrastructure, experienced faculty, and modern curriculum that match global standards. Among various pharmacy colleges, Ambekeshwar Group of Institutions (AGI) stands out as a trusted name for pursuing B.Pharm in Lucknow.

Why Choose Bachelor of Pharmacy?

The Bachelor of Pharmacy (B.Pharm) is a four-year undergraduate degree that opens doors to various career opportunities in the pharmaceutical and healthcare industries. It provides students with essential knowledge about drug composition, chemical structures, pharmacology, and therapeutic uses.

Key benefits of choosing B.Pharm include:

High demand in both public and private sectors

Opportunities in clinical research, drug manufacturing, and quality control

A gateway to pursue higher studies such as M.Pharm, MBA in Pharma, or even a Ph.D.

Eligible to become a registered pharmacist under the Pharmacy Council of India (PCI)

Bachelor of Pharmacy in Lucknow – What Makes It Special?

Lucknow is home to several reputed colleges offering the Bachelor of Pharmacy program. The city combines cultural richness with academic excellence, making it an ideal place for students.

Ambekeshwar Group of Institutions offers a comprehensive and practical-oriented B.Pharm course designed to meet industry standards. The curriculum focuses on both theoretical knowledge and hands-on experience, ensuring students are job-ready upon graduation.

Explore more about the course here: 👉 Bachelor of Pharmacy at AGI, Lucknow

Course Curriculum

The B.Pharm course is divided into 8 semesters over 4 years. The curriculum is as per the guidelines of Dr. APJ Abdul Kalam Technical University (AKTU) and the Pharmacy Council of India (PCI). Subjects include:

Year 1:

Human Anatomy and Physiology

Pharmaceutical Chemistry

Computer Applications in Pharmacy

Year 2:

Pharmacognosy

Pharmaceutical Organic Chemistry

Pathophysiology

Year 3:

Pharmacology

Pharmaceutical Microbiology

Medicinal Chemistry

Year 4:

Clinical Pharmacy

Pharmaceutical Marketing

Research Project and Industrial Training

Apart from classroom lectures, the program includes lab sessions, industry visits, seminars, and internship opportunities in reputed pharmaceutical companies.

Career Opportunities After B.Pharm

Completing a Bachelor of Pharmacy in Lucknow opens up several job opportunities in multiple sectors:

1. Pharmaceutical Industry

Drug Manufacturing and Production

Quality Assurance & Quality Control

Research & Development

Regulatory Affairs

2. Healthcare Sector

Hospital Pharmacist

Community Pharmacy

Clinical Research Associate

3. Government Sector

Drug Inspector

Government Analyst

Jobs in Railways, Army Medical Corps, and Public Hospitals

4. Academics & Further Studies

M.Pharm or MBA

Lecturer or Faculty in Pharmacy Colleges

Research and Ph.D. programs

Top Reasons to Choose AGI for B.Pharm in Lucknow

Ambekeshwar Group of Institutions is considered one of the top private pharmacy colleges in Uttar Pradesh. Here's why:

✅ Modern Infrastructure

Smart classrooms, advanced labs, and digital library

Pharmaceutical Science Block dedicated to hands-on learning

✅ Experienced Faculty

Highly qualified and industry-experienced professors

Regular seminars and workshops with guest experts

✅ 100% Placement Assistance

Strong tie-ups with pharmaceutical companies

Internship and training support throughout the course

✅ Affordable Fee Structure

Scholarships and financial aid for deserving students

Transparent and student-friendly admission process

Learn more about the institution: 👉 Ambekeshwar Group of Institutions – AGI Lucknow

Eligibility Criteria for Admission

To enroll in a Bachelor of Pharmacy program in Lucknow, candidates must meet the following criteria:

Passed 10+2 with Physics, Chemistry, and Biology/Mathematics

Minimum 50% aggregate marks

Valid entrance exam score (if required)

Medical fitness certificate

Final Thoughts

A Bachelor of Pharmacy in Lucknow is not just a degree – it’s your gateway to a dynamic and fulfilling career in healthcare and medicine. With top-notch institutions like Ambekeshwar Group of Institutions, students receive the academic guidance and practical exposure needed to thrive in the pharmaceutical industry.

Whether your dream is to work in a multinational pharma company, become a government officer, or pursue cutting-edge research — a B.Pharm degree from a reputed college in Lucknow can set you on the right path.

Call to Action

👉 Ready to take the next step? Apply now for the Bachelor of Pharmacy in Lucknow at Ambekeshwar Group of Institutions and turn your ambition into achievement.

📍 Visit Campus: Village Kathwara, Bakshi Talab, Lucknow – 226201 📞 Contact: +91-8429999200 | 📧 Email: admission@agi.org.in

[bisresearch0]

In Silico CROs Market Landscape: Trends, Growth & Forecast

In silico contract research organizations (CROs) offer computational services like molecular modeling, virtual screening, bioinformatics, and pharmacokinetic analysis to support pharmaceutical, biotech, and healthcare sectors. Because they make it possible to design drugs that are specific to each patient's genetic profile, these CROs are essential to the advancement of personalized medicine. In silico tools improve drug discovery and development by utilizing technologies including molecular dynamics simulations, machine learning, and artificial intelligence (AI). While AI systems adaptively evaluate enormous amounts of data to predict biological interactions and medication toxicity, machine learning algorithms find patterns and forecast outcomes from large datasets. On the other hand, molecular dynamics simulations provide in-depth understanding of molecular activity, aiding in the development of safer and more efficient treatments.

Market Overview

Market Value (2022): $2.25 billion

Forecasted Value (2032): $12.88 billion

CAGR (2022–2032): 19.06%

Market Drivers

Growing Chronic Illness Burden: The need for technologies that can speed up diagnosis and treatment has increased due to the rising incidence of chronic illnesses worldwide. With the use of AI-powered technologies, in silico CROs are assisting medical professionals in providing more precise and effective care.

Growing In Silico Platform Utilization in Drug Discovery: Pharmacological modeling, structure-activity relationship analysis, and pharmacokinetics are among the early-stage research techniques that pharmaceutical corporations are increasingly depending on computational tools to assist. Compared to conventional techniques, these tools are more effective in identifying novel drug candidates and improving target protein interactions.

Leading Market Players

Take a Deep Dive: Access Our Sample Report to Understand How the market Drive In Silico CROs Competitive Landscape!

Learn more about Healthcare Vertical. Click Here! 

Conclusion

The market for in silico CROs is rising rapidly because to the growing emphasis on personalized medicine and the need for quicker, more affordable drug development. The importance of computational tools was further highlighted by the COVID-19 pandemic, which interrupted many conventional R&D operations.

Key obstacles still face the market, though, most notably the requirement for more precise disease models, particularly for complicated and poorly understood illnesses. While IP laws and infrastructure constraints in developing nations also affect market dynamics, open-source technologies are boosting competitiveness and decreasing margins. Notwithstanding these challenges, ongoing developments in AI and machine learning are improving the precision and functionality of in silico platforms. In silico CROs are positioned to become a key component of future drug development because to continuous innovation and rising worldwide demand.

[keepmovingfourwords]

I still remember how Little Man Photo Repository was convinced I wasn’t like them so they started rubbing the song in my face when it came out and I was sharing aid posts and brought in two strangers, one who listened to Kanye a lot, to make it hurt more.

They didn’t know I was the K-dot to their Drake. Or did they? Idk…..

It was some very Drake behavior for some colonizers to get so hype over a song that doesn’t like them during a genocide that I was working very hard to oppose all by myself (in the emotional psychological time-consuming attention-consuming sense) right in front of them.

It was very Drake of them not to get it.

They had no idea I was enjoying Kendrick’s side of it because I wasn’t as performative and ostentatious. And I also never listened to Drake’s side because it was a waste of time to me.

I found out he was a human trafficker in 204-2016. Cannot remember which year. It was public knowledge. Kendrick being able to call him out for it was a good sign that human traffickers were finally losing power.

I feel like that was enough torture for LMPR. They can reform from their white liberalism.

I’m not going to compliment Little Man’s brother anymore until I see some real improvement. He was better at hiding what I didn’t like from me since he had access to my thoughts and feelings. He was better at appealing to me because he could see my thoughts and feelings the whole time

It’s not your fault you turned out this way. You suffer from white supremacy too. But you also chose it to gain. And my thoughts and feelings didn’t stop you from being materialistic, even though you knew I cared while you deliberately tried to spite me while I supported Palestinians. Who you never donated to? I’m assuming.

You didn’t have to brag about everything you bought in front of me during a genocide.

You knew why Johnny Depp was bad to me. You knew why it hurt me.

There are no nonbelievers in the depths of a fox hole.

🦊🕳️

You thought my feelings weren’t real because I was designed to be like pleasure and emotional support tech? Or no. You were just a misogynist who knew I’d be easy to use if you could get your hands on the remote.

You shouldn’t watch Breaking Bad and BCS if you don’t really care about drug addicts and Indigenous people.

You can get better. You should be Indigenous too. And you still can be. But first you have to suffer a little more.

Yeah. Figure out your ancestors’ culture(s) instead of trying to run away to Japan.

💚

[govindhtech]

Trapped-Ion Quantum Computing Solved Protein Folding Issues

Ion-trapped quantum computing

Quantum Computing Solve Complex Protein Folding and Optimisation Problems

Quantum computing advanced when researchers developed a new quantum algorithm on trapped-ion computers to solve combinatorial optimisation and protein folding problems. This work is the largest quantum hardware implementation of protein folding and shows how quantum systems can outperform traditional computers on difficult problems.

The work by Kipu Quantum GmbH and IonQ Inc. used a 36-qubit trapped-ion processor to mimic protein folding for peptides up to 12 amino acids. Computational biology still struggles to reliably predict protein structures, which affects materials research and medication development. For this complex situation, classical approaches are limited.

The application of non-variational bias-field digitised counterdiabatic quantum optimisation (BF-DCQO). This method uses trapped-ion systems' intrinsic all-to-all connection to explore the solution space of difficult higher-order unconstrained binary optimisation (HUBO) issues.

HUBO problems are difficult optimisation challenges. The BF-DCQO method solves challenging HUBO problems optimally on fully connected trapped-ion quantum processors. This method always worked best for dense HUBO situations.

Protein folding and fully connected spin-glass and MAX 4-SAT problems were utilised to demonstrate the algorithm's flexibility on all 36 qubits. Interestingly, they resolved MAX 4-SAT problems during the computational phase changeover. The quantum algorithm's resolution of this phase transition, a tough point for classical algorithms, shows its capacity to address problems approaching the boundaries of classical computation. This suggests that quantum algorithms may outperform regular methods for certain tasks.

The non-variational character and solution space navigation technique of the BF-DCQO algorithm are notable. BF-DCQO may be more deterministic than probabilistic quantum methods for specific problem classes. This direct method reduces repeated measurements and post-processing, improving computer efficiency.

Modelling protein folding systems with 12 amino acids is a major advancement, exceeding earlier quantum hardware implementations and increasing computing power. The quantum technique improves these computationally intensive simulations, allowing researchers to study protein structures in unprecedented depth.

Researchers meticulously constructed and polished the BF-DCQO algorithm to maximise trapped-ion quantum processor power. All-to-all connectivity allows complex quantum circuits to avoid topologies with sparser connections. The algorithm's error characteristics demonstrated that it is robust to numerous types of faults, making it suitable for noisy quantum devices. Additionally, strategies were developed to mitigate the main error causes.

This research suggests that the BF-DCQO algorithm can provide a quantum advantage for dense HUBO challenges, especially when combined with scalable trapped-ion quantum devices. The demonstration that quantum computing can outperform classical algorithms on problems that conventional computers cannot solve marked a turning point in its progress. The algorithm's adaptability to a variety of optimisation problems shows its promise to solve real-world challenges in drug development and other fields like financial modelling.

Scalability was considered to make the method compatible with larger quantum processors without major adjustments. The team is developing ways to spread the algorithm over multiple quantum processors to boost its scalability. The BF-DCQO algorithm's implementation has been meticulously documented to aid future research and instruct other researchers who want to replicate the findings.

The researchers believe quantum technology and algorithm design will enable future answers to much larger and more difficult problems. This constant evolution should enable new technical and scientific advances.

Quantum Zeitgeist, an online journal covering quantum computing news, research, and breakthroughs, covered this discovery. The book helps researchers and businesses understand and use quantum computing to solve insoluble problems in various industries. This work, which employs quantum mechanics to do complex computations tenfold faster than traditional computers, supports the publication's mission of covering how quantum technologies are changing the future.

[amairadutta]

The Impact of AI in Edge computing and Data centers

Introduction

The world as we know it is rapidly Digitizing and transforming with the prevalence of AI. One of the many areas being impacted by AI is Edge Computing and Data Centers. Data processing becomes the central idea, with data being at the tips of your fingers and everywhere you see. Technology helps in delivering data and opens doors to heightened efficiency. This blog revolves around Artificial Intelligence’s effect on edge computing and data centers.

Edge computing

This computer model brings power to data processing. It refers to networks or devices near a user. What it does is process the generated data faster, leading to high action results. It has numerous advantages, including improvement of physical asset management and creative novel and innovative human experiences. Internet of Things (IoT) devices, sensors, and vehicles are edge devices. It can improve customer engagement, retail experiences, and upskilling of workers. To combine the prowess of Artificial Intelligence and edge computing, we have edge AI.

Edge AI

It brings speedy performance in computing abilities to edge computing. It allows Artificial Intelligence applications to run directly on devices and handles data processing in milliseconds. This helps in bringing efficiency to autonomous vehicles or devices that require proactive and decisive decision-making. This also protects sensitive data and is energy-efficient. Security cameras with this technology can assess suspicious activities effectively, while industrial applications benefit from monitoring and adjustments in production processes in real time.

Talking about autonomous vehicles brings to the forefront the need for the combined working of Industrial IoT and edge computing. This vehicle has to detect activity and scenarios of pedestrians, vehicles, roads, and street signs, and monitor its system.

Industrial IoT

It refers to the employment of smart devices in industrial applications. Industrial IoT makes use of sensors and smart devices to capture, move, and detect changes in levels of temperature. It also helps in automating procedures for efficiency, safety, data delivery for analysis, and making sure all the processes happen promptly.

Quantum computing

Apart from edge computing, this technology makes use of quantum theory. This can enhance the way challenges in finance and logistics are dealt with. It can solve complex problems that traditional computers are unable to do. Quantum computing can help in building effective investment portfolios in retail and finance. It can create improved trading simulators and detect fraud effectively. In the healthcare industry, it can be deployed to discover new drugs and aid in the curation of genetically focused treatment. This can revolutionize the research in DNA. In security systems, this technology can design enhanced data encryption for increased security. It can form better ways to employ light systems to signal house or property intrusion by trespassers. It can be utilized for planning for aircraft and traffic. Quantum computing is a fairly new development in progress that uses quantum computers.

Cloud computing

This makes use of traditional computers unlike the previous one. It is a delivery model that boosts the efficiency of AI. AI helps in automating processes, improves data management, aids in strategic decision-making, makes effective data analysis, and enhances security. AI helps in the reduction of manual errors and provides better interconnectedness between users. It also facilitates in generating traffic to draw in more visitors to the website.

Natural language processing

To understand how AI as a technology is a game-changer, let’s look at NLP. This is a branch of AI that helps how we interact with computers and effectively interpret textual data. Natural language processing enables computers to make sense of data and generate very human-like communication and data. Natural language processing produces natural language by amalgamating techniques from machine learning, computer science, and linguistics to provide personalized, contextual, and precise support to users. NLP can aid as a virtual assistant who caters to the user’s questions, fulfills tasks, and offers answers to questions in a natural language. It also facilitates global connectivity by enabling cross-communication across different languages. It is also an effective means of assessing market and consumer needs by providing sentiment analysis.  

Edge machine learning

Machine learning is a subdivision of Artificial Intelligence. Edge computing can be combined with AI or ML applications on edge devices such as sensors and devices to get prompt data processing. It allows for effective collection, analysis, identification of patterns, and initiation of fast action without the dependence on traditional cloud networks.

Impact of Artificial Intelligence on Data Centers

To optimize the power of AI, rigorous computing and large data storage are required. Data centers are physical locations containing computing infrastructure like servers, network equipment, drives for data storage, and more. These help businesses to bring their growing equipment that may be distributed across various locations and branches in one place. This increases cost-effectiveness and allows businesses to get help from third-party data centers. The various advantages of data centers are that they provide backup power supplies, replicate data across machines in case of a disaster, provide facilities with controlled temperatures to increase the life expectancy of the machines, and more effective security measures can be implemented in line with data laws. AI and machine learning are taking data center operations to newer heights. AI helps manage resources through predictive analysis. AI helps with cost savings, improved delivery, work management, and security against power outages.

Network Infrastructure

To supplement the advantages of Artificial Intelligence, network infrastructure comes into the picture. This refers to systems, devices, and applications that help in communication between users. In organizations, admins can monitor data access, ensuring increased security. This allows for sound monitoring of any issues that may arise in the network, leading to decreased disruptions in operations and ensuring the overall productivity of the organization. AI Data Center Network is a specialized network infrastructure curated to enrich the powers of AI and Machine Learning. This allows for a maximized ability to handle a load of data and fast data processing.

Conclusion

The synergy of computing and AI can create a dynamic workforce, increasing efficiency and processing large amounts of data in a short amount of time. The scalability and productivity of IT infrastructure will skyrocket with the precision and ability of AI to simplify complex data and provide beneficial insight. Organizations will profit from the united forces of cloud, edge, and quantum computing and AI coming together to increase the capacity to process data. Streamlining operations will be a smooth-sailing business allowing for more productivity and innovation.

[faithfullysolitaryveteran]

Why Choose an Alcohol Rehabilitation Center? Understanding the Benefits of Professional Help

Introduction

When faced with the demanding situations of dependancy, many folks and their families discover themselves at a crossroads. The resolution to search assist will be daunting, yet figuring out the blessings of professional medication can illuminate the trail in the direction of recuperation. In this text, we’ll delve into the myriad blessings of selecting an Alcohol Rehabilitation Center, highlighting how these centers provide major toughen, resources, and information quintessential for overcoming dependancy. From cleansing tactics to holistic strategies, we purpose to empower readers with know-how approximately why professional support will never be simply invaluable yet mostly critical in the adventure to sobriety.

Why Choose an Alcohol Rehabilitation Center? Understanding the Benefits of Professional Help

Choosing an alcohol rehabilitation center comes to recognizing that battling addiction is a complicated tour that occasionally calls for greater than dedication by myself. These devoted centers provide dependent programs designed to address the multifaceted nature of alcoholism. Here’s a more in-depth inspect a few compelling motives why attempting assist from pros in a rehabilitation center is valuable.

1. Comprehensive Treatment Plans

At a respectable rehabilitation center, members get hold of tailored therapy plans that understand their different wishes and situations. This personalized technique guarantees that each aspect of dependancy—physical, mental, and emotional—is addressed comprehensively.

1.1 Individual Assessment and Diagnosis

Before therapy begins, customers primarily bear thorough exams to ensure their degree of addiction and any co-happening intellectual well being disorders. This preliminary analysis is https://maps.app.goo.gl/9r87aRtGcdVUYhvt6 indispensable as it informs the custom remedy strategy.

1.2 Evidence-Based Therapies

Professional facilities traditionally appoint facts-established treatments along with Cognitive Behavioral Therapy (CBT) and Dialectical Behavior Therapy (DBT). These ways have been confirmed victorious in dealing with addiction and its underlying causes.

2. Medical Supervision During Detoxification

Detoxification is generally the first step in recuperation, and doing so less than clinical supervision vastly reduces negative aspects associated with withdrawal symptoms.

2.1 Safe Withdrawal Management

In a credible drug rehabilitation center, experienced medical body of workers intently computer screen clientele for the period of detox to arrange symptoms safely and effortlessly.

2.2 Medication-Assisted Treatment (MAT)

Some rehabilitation centers present MAT, by means of medications like naltrexone or acamprosate to aid curb cravings and withdrawal signs, making it easier for persons to focal point on recuperation.

3. Therapeutic Support from Trained Professionals

Having access to educated pros who concentrate on addiction recovery makes a widespread distinction in medication outcomes.

3.1 Psychologists and Counselors

Licensed psychologists and counselors provide personal medical care classes that handle non-public struggles related to alcohol dependence

[sandeepmellacheruvu]

The Evolution of Medical Simulation

Just as pilots use flight simulators to learn how to handle various flight scenarios, doctors have medical simulators to aid in their education and skill development. In both aviation and healthcare, errors can have grave consequences. In health care, preventable medical errors are among the leading causes of death in the US, resulting in up to 250,000 deaths each year, according to a Johns Hopkins study.

Medical simulators imitate real-world situations, helping healthcare providers learn, practice, and evaluate their medical skills in a low-stakes environment. Also called simulation-based training (SBT), medical simulators allow surgeons to practice open-heart procedures on computerized mannequins. However, the idea of refining medical skills through various physical simulations goes back more than three millennia.

For example, stone carvings of the human form that appear to be medical related date back to 24,000 BC. The oldest written evidence of medical simulation was Sushruta Samhita, which described the use of wooden objects to train and practice surgery for wounds. By the 6th century BC, surgical models more closely mimicked human bodies and could leak fluid.

In 960 CE-China, physicians used bronze statues to teach anatomy and acupuncture. The life-sized bronze mannequins had organs and openings for injection. The mid-18th century saw the development of the glass uterus, used to teach birthing to surgeons and midwives. It also marked the beginning of modern medical simulation with the formal recognition of SBT in the medical community.

In the 1960s, Dr. Peter Safar and anesthesiologist Bjorn Lind convinced Asmund Laerdal, a Norwegian plastic doll maker, to design and produce Resusci-Anne, a life-sized head and torso. They used the model to teach the head tilt/chin lift maneuver and mouth-to-mouth rescue breaths for relieving airway obstruction. Later, they added a spring mechanism to the mannequin to teach chest compression. That's how the first cardiopulmonary resuscitation (CPR) mannequin came into being.

Another notable milestone in medical simulation came in 1968 when Michael Gordon, MD, of the University of Miami, developed Harvey, a cardiology patient simulator. Harvey can simulate nearly all cardiac diseases by relaying varying blood pressure, pulse, and auscultatory findings. Several medical schools still use Harvey to teach the physical diagnosis of heart problems.

Resusci-Anne and Harvey embody the two major families of SBT: task trainers and diagnostic trainers. Task trainers teach treatment techniques, like how to inject a drug using a needle and perform minimally invasive surgery. Diagnostic trainers aid the understanding of patient information, such as heart sounds and imaging.

As computer capacities and accessibility increased, so did simulator complexity and capabilities. For example, Stanford University researchers developed the Comprehensive Anesthesia Simulation Environment (CASE) which, as part of a mannequin, can produce all the vital readings typically found in a patient under anesthesia. Such developments led to a different approach to SBT.

The new approach, environment trainers, focuses on existing information and skill application in predetermined conditions. Task trainers and diagnostic trainers emphasized the attainment of new skills and information.

With the shift to environment trainers came advanced applications such as virtual reality (VR), augmented reality (AR), and mixed reality. VR lets users operate in computer-generated environments, manipulating 3D mannequins. AR brings virtual environments closer to reality while keeping the real world central in the simulation. Mixed reality eliminates the real-virtual barrier, allowing users to operate in both environments simultaneously.

Medical simulations may not precisely reflect the realities of having a patient on an operating table. However, simulating a maneuver until an acceptable degree of training is achieved, coupled with real-time feedback, boosts skill and engenders confidence. Simulations are also helpful for maintaining skills necessary for handling infrequent situations, allowing physicians to respond with confidence whenever such situations arise.

[cybersecurityict]

Artificial Intelligence Market Size, Share, Analysis, Forecast, and Growth Trends to 2032: Cloud AI and Edge Computing to Drive Moment

The Artificial Intelligence Market was valued at USD 178.6 Billion in 2023 and is expected to reach USD 2465.8 Billion by 2032, growing at a CAGR of 33.89% from 2024-2032.

Artificial Intelligence Market is advancing at a remarkable pace, transforming industries with automation, predictive analytics, and intelligent decision-making. From finance and healthcare to retail and manufacturing, AI applications are reshaping business models across the globe, with the USA and Europe leading the charge through significant investments and rapid tech adoption.

Analyze key drivers shaping the AIoT market across the United States

Artificial Intelligence Market continues to fuel innovation and productivity as organizations turn to AI for operational efficiency and customer personalization. With scalable cloud solutions and widespread access to machine learning tools, companies are building smarter, data-driven ecosystems that redefine competitive advantage.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/2801 

Market Keyplayers:

Google (Alphabet Inc.) - Google AI

IBM - IBM Watson

Microsoft - Azure AI

Amazon Web Services (AWS) - AWS Deep Learning AMIs

NVIDIA Corporation - NVIDIA DGX Systems

Intel Corporation - Intel Nervana

Baidu, Inc. - Baidu AI

Salesforce - Salesforce Einstein

Apple Inc. - Siri

Tencent - Tencent AI Lab

SAP - SAP Leonardo

Adobe Inc. - Adobe Sensei

OpenAI - GPT-3

Market Analysis

The Artificial Intelligence Market is being driven by a surge in enterprise digital transformation, increased computing power, and the availability of vast data sets. AI technologies, including natural language processing, computer vision, and deep learning, are unlocking new capabilities in automation, risk detection, and human-machine collaboration.

The USA leads global AI development with strong tech infrastructure and investment, while Europe balances innovation with a regulatory-first approach, emphasizing ethical AI and data privacy.

Market Trends

Growth of generative AI in content creation, coding, and design

Integration of AI with IoT, robotics, and edge computing

Increased adoption of AI chatbots and virtual assistants in customer service

Expansion of AI-powered cybersecurity tools for threat detection

Surge in AI for healthcare diagnostics and drug discovery

Use of AI algorithms for hyper-personalized marketing

Democratization of AI tools through no-code and low-code platforms

Market Scope

The scope of the Artificial Intelligence Market is expanding rapidly as AI moves from experimental labs to core business functions. Companies are embedding AI into everything from supply chains to customer experiences, setting new benchmarks for speed and intelligence.

Predictive maintenance in manufacturing

AI-enhanced financial forecasting and fraud detection

Smart assistants for business operations

AI in autonomous systems (vehicles, drones)

Talent acquisition powered by AI screening tools

Real-time language translation and transcription

AI-aided legal research and contract analysis

Forecast Outlook

The Artificial Intelligence Market is poised for exponential growth as businesses, governments, and consumers embrace intelligent systems that learn, adapt, and evolve. With advancements in multimodal models and AI-as-a-service platforms, the path ahead is one of deep integration and rapid deployment. The market’s future will be defined by ethical frameworks, regulatory alignment, and the race to build trusted, scalable solutions. USA and Europe remain pivotal arenas, shaping global standards and accelerating AI maturity across sectors.

Access Complete Report: https://www.snsinsider.com/reports/artificial-intelligence-market-2801 

Conclusion

The Artificial Intelligence Market is not just a technological revolution—it’s a transformation of how the world works, learns, and innovates. In an era defined by speed, data, and intelligence, AI stands at the center of progress.

Related Reports:

How AI is Transforming Call Centers Across the US: Market Analysis and Forecast

Analyze key drivers shaping the AIoT market across the United States

About Us:

SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.

Contact Us:

Jagney Dave - Vice President of Client Engagement

Phone: +1-315 636 4242 (US) | +44- 20 3290 5010 (UK)

[bjmadan12]

Best Quality precision oncology companies

Advancements in Computer Precision Oncology: A Global Overview

Precision oncology has emerged as a transformative approach in cancer treatment, leveraging cutting-edge technologies to tailor therapies to individual patients. This paradigm shift is driven by advancements in artificial intelligence (AI), genomics, and radiopharmaceuticals, enabling more effective and personalized cancer care. Below is an overview of leading companies and initiatives in this field, with a reference to B. J. Madan & Co., a prominent player in India.

1. B. J. Madan & Co. – Bridging Research and Reality in India

B. J. Madan & Co., based in New Delhi, India, has been at the forefront of integrating research with clinical application in the realm of precision oncology. With over seven decades in pharmaceuticals and more than 15 years in nuclear medicine, the company specializes in radiopharmaceuticals and molecular imaging. Their focus on theranostics—combining therapy and diagnostics—has positioned them as a key player in personalized cancer treatment in India. 

The company offers comprehensive services, including preclinical and clinical research, trial management, and regulatory support, facilitating the development and commercialization of radiopharmaceutical products. Their expertise spans all phases of clinical trials, ensuring robust data collection and analysis. Through collaborations with academic institutions and hospitals, B. J. Madan & Co. aims to bring innovative cancer therapies to market, addressing the unique needs of the Indian population.

2. Tempus AI – Pioneering Data-Driven Precision Medicine

Tempus AI, headquartered in Chicago, USA, utilizes AI and machine learning to analyze clinical and molecular data, aiding in the development of personalized cancer treatments. Their platform integrates genomic sequencing with clinical data to provide insights into treatment options and potential outcomes. In June 2024, Tempus went public on Nasdaq, reflecting its growing influence in the field of precision oncology. 

3. Sophia Genetics – Enhancing Genomic Analysis

Sophia Genetics, based in Switzerland, offers a platform that combines genomic and radiomic data to support clinical decision-making in oncology. Their technology enables hospitals and laboratories to analyze complex data sets, improving the accuracy of cancer diagnoses and treatment plans. With over 2 million genomic profiles analyzed, Sophia Genetics has established itself as a leader in data-driven oncology. 

4. Insilico Medicine – Accelerating Drug Discovery

Insilico Medicine, located in Boston, USA, employs AI and deep learning to expedite drug discovery processes. Their platform focuses on identifying novel therapeutic targets and biomarkers, streamlining the development of cancer treatments. By integrating big data analysis with genomics, Insilico Medicine aims to reduce the time and cost associated with bringing new cancer therapies to market. 

5. SimBioSys – Virtualizing Tumor Environments

SimBioSys, based in Illinois, USA, has developed TumorScope, a simulation engine that creates virtual replicas of individual tumors using standard diagnostic data. This technology allows for the prediction of therapeutic responses and the identification of potential resistance mechanisms. By providing a detailed understanding of tumor behavior, SimBioSys aids clinicians in making informed treatment decisions. 

6. Owkin – Advancing AI in Drug Development

Owkin, a French-American company, specializes in applying AI to biomedical research, particularly in oncology. Through partnerships with major pharmaceutical companies like Sanofi and Bristol-Myers Squibb, Owkin develops predictive models to improve clinical trial designs and identify new therapeutic targets. Their collaborative approach accelerates the development of precision therapies. 

7. Epigene Labs – Innovating with mCUBE Platform

Epigene Labs, operating in Paris and Boston, focuses on precision oncology through the use of artificial intelligence and genomics. Their mCUBE platform integrates multi-dimensional genomic data to identify drug targets, biomarkers, and patient selection criteria, facilitating the development of personalized cancer treatments. 

8. Sophia Genetics – Expanding Global Reach

Sophia Genetics continues to expand its global presence, collaborating with institutions like Memorial Sloan Kettering Cancer Center and companies such as AstraZeneca to enhance access to genomic analysis tools. Their partnerships aim to democratize precision oncology, ensuring that advanced diagnostic capabilities are available to a broader patient population. 

9. BioNTech – Investing in Personalized Cancer Vaccines

BioNTech, known for its role in developing the Pfizer COVID-19 vaccine, is investing £1 billion in UK research centers to advance personalized medicine, including cancer vaccines. The initiative includes the establishment of an AI hub in London and a genomics and oncology research center in Cambridge, underscoring BioNTech's commitment to integrating AI with mRNA technologies for personalized cancer treatments. 

10. Recursion – Merging AI with Drug Discovery

Recursion, a U.S.-based pharmaceutical company, acquired Exscientia, a British biotechnology firm, to enhance its AI-powered drug discovery capabilities. The merger aims to combine Exscientia's expertise in molecule design with Recursion's production capabilities, accelerating the development of cancer therapies. 

Conclusion

The landscape of precision oncology is rapidly evolving, with companies worldwide leveraging AI, genomics, and innovative technologies to develop personalized cancer treatments. B. J. Madan & Co. stands out in India for its integration of radiopharmaceuticals and molecular imaging, contributing significantly to the advancement of precision oncology in the region. As global collaborations and technological advancements continue, the future of cancer treatment looks increasingly personalized and effective.

Visit:- https://www.bjmadan.com/precision-oncology.html

[sab-cat]

May 20, 2025

Three-dimensional printing is transforming medical care, letting the health care field shift from mass-produced solutions to customized treatments tailored to each patient’s needs. For instance, researchers are developing 3D-printed prosthetic hands specifically designed for children, made with lightweight materials and adaptable control systems.

These continuing advancements in 3D-printed prosthetics demonstrate their increasing affordability and accessibility. Success stories like this one in personalized prosthetics highlight the benefits of 3D printing, in which a model of an object produced with computer-aided design software is transferred to a 3D printer and constructed layer by layer.

We are a biomedical engineer and a chemist who work with 3D printing. We study how this rapidly evolving technology provides new options not just for prosthetics but for implants, surgical planning, drug manufacturing, and other health care needs. The ability of 3D printing to make precisely shaped objects in a wide range of materials has led to, for example, custom replacement joints and custom-dosage, multidrug pills.

[marcoluther]

How Generative AI is Accelerating Healthcare Research and Development

Healthcare research and development (R&D) have always been at the forefront of technological innovation, driven by the need to develop better treatments, faster diagnostics, and personalized care. Today, one of the most transformative technologies in this space is Generative Artificial Intelligence (Generative AI). From drug discovery to medical imaging and personalized medicine, Generative AI is revolutionizing how researchers and healthcare professionals develop new solutions for complex medical challenges.

In this blog, we will explore the many ways Generative AI is accelerating healthcare R&D, the benefits it brings, real-world applications, and the future potential of this exciting technology.

What is Generative AI?

Before diving into its impact on healthcare, it’s essential to understand what Generative AI for Healthcare actually is. Generative AI refers to a subset of artificial intelligence techniques that can create new content, whether images, text, molecular structures, or even code, based on patterns learned from existing data.

Unlike traditional AI that simply classifies or predicts based on input data, Generative AI models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and large language models like GPT can produce novel outputs that mimic human creativity. This ability to generate new, original data makes it a powerful tool for innovation in healthcare research.

Accelerating Drug Discovery

Faster Identification of Drug Candidates

One of the most time-consuming and costly aspects of healthcare R&D is drug discovery. Traditional methods involve screening thousands of compounds, running countless lab experiments, and conducting lengthy clinical trials. Generative AI is transforming this by enabling rapid in silico (computer-simulated) design of new drug candidates.

Generative models can analyze massive chemical databases to learn the properties of effective drug molecules and then generate entirely new molecular structures optimized for specific targets, such as proteins involved in diseases. This dramatically reduces the time from concept to candidate molecule, allowing researchers to focus on the most promising compounds for further testing.

Predicting Drug Properties and Side Effects

Beyond designing new molecules, Generative AI can simulate how these compounds will interact with biological systems. This includes predicting pharmacokinetics (how the drug moves through the body), potential side effects, and toxicity levels. Early detection of adverse effects can save valuable time and resources by filtering out unsafe drug candidates before clinical trials.

Case Study: COVID-19 Drug Development

During the COVID-19 pandemic, Generative AI played a key role in accelerating the search for antiviral drugs. Several AI-powered platforms were used to generate and screen molecules targeting the virus’s proteins, significantly speeding up the initial phases of drug discovery compared to traditional methods.

Enhancing Medical Imaging and Diagnostics

Generating Synthetic Medical Images for Training

Medical imaging, including MRI, CT scans, and X-rays, is critical for diagnosing many conditions. However, training AI diagnostic systems requires vast amounts of labeled imaging data, which can be difficult to obtain due to privacy concerns and limited availability.

Generative AI can create synthetic yet realistic medical images to augment existing datasets. These artificially generated images help train diagnostic models to recognize diseases more accurately, even when real data is scarce. This leads to improved diagnostic performance and more reliable clinical decision support.

Image Reconstruction and Enhancement

Generative AI also aids in improving the quality of medical images. For example, GANs can reconstruct higher-resolution images from low-quality scans, reduce noise, or even fill in missing parts of images. Enhanced image clarity allows radiologists and clinicians to detect abnormalities earlier and with greater confidence.

Automated Diagnosis

Large language models combined with image-generating models are increasingly being used to automate diagnosis based on imaging data. These systems can identify patterns associated with diseases such as cancer, stroke, or retinal conditions, potentially providing quicker and more accurate diagnosis to support clinical workflows.

Personalized Medicine and Treatment Optimization

Tailoring Therapies to Individual Patients

Every patient’s biology is unique, which is why personalized medicine has become a major focus in healthcare R&D. Generative AI can analyze vast datasets including genomics, proteomics, and clinical records to design personalized treatment plans that are tailored to the patient’s specific genetic profile and disease characteristics.

For example, generative models can simulate how a patient might respond to different drug combinations, helping doctors select the most effective and least toxic therapy.

Predicting Disease Progression

In chronic diseases like cancer or neurodegenerative disorders, predicting disease progression is crucial for timely intervention. Generative AI models can generate simulations of how a disease might evolve in an individual, helping clinicians anticipate complications and adjust treatments proactively.

Accelerating Clinical Trials

Synthetic Patient Data Generation

Recruiting patients for clinical trials can be a bottleneck in healthcare research, often delaying the development of new treatments. Generative AI can create synthetic patient data that mimics real-world clinical characteristics without compromising patient privacy. This synthetic data allows researchers to test hypotheses and design trials more effectively.

Optimizing Trial Design

By simulating how different patient groups might respond to treatments, Generative AI can help design more efficient and targeted clinical trials. This reduces the time and cost associated with testing new drugs and therapies, bringing life-saving treatments to market faster.

Drug Repurposing and Rare Diseases

Finding New Uses for Existing Drugs

Drug repurposing—finding new therapeutic uses for already approved drugs—is an attractive approach because it bypasses many safety concerns and speeds up the time to clinical use. Generative AI models analyze existing drug databases and biological pathways to generate hypotheses for new drug-disease associations, accelerating repurposing efforts.

Addressing Rare Diseases

Rare diseases often suffer from limited research due to small patient populations. Generative AI’s ability to create synthetic data and simulate biological processes allows researchers to study these diseases more deeply, helping to identify new treatment options and improve patient outcomes.

Overcoming Data Challenges in Healthcare

Data Privacy and Security

Healthcare data is sensitive, and sharing it for research is often restricted by privacy laws. Generative AI models can generate realistic synthetic data that preserves patient privacy while enabling researchers to perform meaningful analysis. This balance between data utility and privacy is crucial for advancing healthcare research ethically.

Integrating Multimodal Data

Healthcare data comes in many forms: clinical notes, lab results, medical images, genomic sequences, and more. Generative AI excels at integrating these diverse data types to generate holistic models of patient health, enabling deeper insights and more comprehensive research outcomes.

The Future of Generative AI in Healthcare R&D

Continuous Learning and Adaptation

Generative AI models are evolving to become more adaptive, capable of continuous learning from new data. This means healthcare research can remain agile, incorporating the latest discoveries and patient information to refine models and predictions in real-time.

Collaborative Research Platforms

We can expect the emergence of collaborative AI platforms that enable researchers worldwide to share generative models and data securely. This democratization of AI tools will accelerate innovation and reduce duplication of efforts.

Ethical and Regulatory Considerations

As Generative AI becomes more integrated into healthcare R&D, ensuring ethical use and regulatory compliance will be paramount. Transparent AI development, explainability of AI-generated outputs, and adherence to medical standards will help build trust among clinicians, patients, and regulators.

Conclusion

Generative AI is proving to be a powerful catalyst in accelerating healthcare research and development across multiple fronts—from drug discovery and medical imaging to personalized medicine and clinical trial optimization. By generating novel data, simulating biological processes, and enabling more efficient workflows, Generative AI is helping researchers overcome traditional bottlenecks, reduce costs, and bring innovative treatments to patients faster than ever before.

While challenges remain around data privacy, ethical use, and regulatory frameworks, the potential benefits of Generative AI in transforming healthcare R&D are immense. As the technology continues to advance, we are likely to witness a new era of precision medicine and breakthrough therapies that improve health outcomes worldwide.