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Posted 9/23/2023, 1:52:00 AM

Machine Learning Dramatically Speeds Up Virtual Drug Screening, Processes Billions of Molecules

Researchers used machine learning to speed up virtual drug screening by 10x, processing 1.56 billion drug-like molecules.
The team collaborated with industry and leveraged supercomputers for one of the world's largest virtual drug screens.
Machine learning accurately predicted top scoring compounds from just 1% of the full library as training data.
The study represents the first large-scale comparison of machine learning vs conventional docking approaches.
The researchers released benchmark datasets to advance computational drug discovery tools.

news-medical.net

Relevant topic timeline:

8/18/2023

Schrödinger’s Equation: Physics + Machine Learning = Drug Discovery

### Summary Schrödinger, a drug discovery company, combines physics and machine learning to develop new drugs. The company collaborates with other drug developers and utilizes AI and physics-based principles to identify new drugs for various diseases. ### Facts - Schrödinger sees traditional chemists, not other AI-based drug discovery companies, as its competition. - The company combines AI with physics-based principles to identify new drugs and targets for various diseases. - Schrödinger relies on machine learning to generate large amounts of data for training its models, as physics-based calculations are slow. - Schrödinger's drug discovery efforts are supported by physics and machine learning. - The company has over 1,750 customers for its drug discovery software and 13 active collaboration projects with biopharma partners. - Schrödinger's partners include Bristol Myers Squibb, Eli Lilly, and Takeda Pharmaceutical. - The company also has its own pipeline of 19 active programs, with the first candidate entering clinical trials in 2022. - Schrödinger's candidates include inhibitors for MALT1, CDC7, and Wee1 genes, with plans to move more programs into clinical trials in the next decade. - The company has a chief medical officer responsible for clinical development and regulatory strategy. - Schrödinger reported net income of $4.3 million in Q2 2023 and lowered its guidance for drug discovery revenue, citing delays in milestone achievements by collaboration partners. - The company's software revenue remained stable and Schrödinger raised its guidance for 2023. 🧪💻🧬🔬💊🔍

8/29/2023

The company landscape for artificial intelligence in large-molecule drug discovery

Artificial intelligence (AI) techniques, particularly machine learning, are increasingly being used in drug research and development (R&D), with applications expanding beyond small molecules to include large-molecule modalities such as antibodies, gene therapies, and RNA-based therapies. These therapies, which make up a significant portion of the biopharma industry's current and future commercial potential, are expected to represent approximately 50% of the oncology market by revenue in 2030, with the majority coming from antibodies.

9/2/2023

How the Medical Industry Could Produce the Biggest AI Stock Winners

Artificial intelligence has the potential to revolutionize the medical industry by quickly discovering new drug candidates and extending human lifespans through therapies that repair damage to cells and tissues, leading to a projected $50 billion AI drug discovery revolution and the possibility of living to 150 years old.

9/4/2023

Machine learning for chemistry: Basics and applications

A review published in Engineering explores the potential of machine learning (ML) in revolutionizing chemical research, providing insights into popular ML algorithms and their applications in chemistry.

9/8/2023

Machine Learning Model Predicts Drug Approval Odds Before Clinical Trials

A research team at Pohang University of Science and Technology (POSTECH) has developed a machine learning approach to predict drug outcomes and side effects before clinical trials, using gene perturbation effects to evaluate the discrepancies between cells and humans.

9/12/2023

Machine Learning Algorithms Optimize Antibody Drugs by Predicting Modifications

Machine-learning algorithms developed at the University of Michigan can identify problem areas in antibodies that cause them to bind with non-target molecules, enabling researchers to modify the antibodies and optimize their effectiveness in fighting diseases like Parkinson's, Alzheimer's, and colorectal cancer.

9/15/2023

MIT Lab Uses Inspiration from Nature and Automation to Revolutionize Therapeutics Discovery

Professor Brad Pentelute and the Pentelute Lab at MIT are using a combination of chemistry, biology, and engineering to develop new techniques and platforms that have the potential to revolutionize therapeutics, including using nature-inspired research to solve protein delivery problems and building an automated protein printing machine. They are also using machine learning and automation to discover new peptides and proteins that can be used in cancer treatment and other applications, with the goal of rapidly designing molecules with new functions and advancing the field of AI-driven molecule design.

9/19/2023

Pharma Collaboration Could Accelerate AI-Driven Protein Drug Development

The use of generative AI, combined with federated and active learning, can accelerate the development of protein drugs by improving predictions of drug properties and enabling collaboration among biopharmaceutical companies while protecting their competitive interests.

9/20/2023

How AI Could Revolutionize Scientific Research

Scientists are increasingly using AI tools for various scientific tasks, including drug discovery and protein shape prediction, and self-driving lab robots are seen as a potential means to further enhance research efficiency.

9/22/2023

AI Speeds Up Patient Recruitment and Reduces Costs of Clinical Drug Trials

Major drugmakers are using artificial intelligence (AI) to accelerate drug development by quickly finding patients for clinical trials and reducing the number of participants needed, potentially saving millions of dollars. AI is increasingly playing a substantial role in human drug trials, with companies such as Amgen, Bayer, and Novartis using AI tools to scan vast amounts of medical data and identify suitable trial patients, significantly reducing the time and cost of recruitment. The use of AI in drug development is on the rise, with the US FDA receiving over 300 applications that incorporate AI or machine learning in drug development from 2016 through 2022.

9/26/2023

Machine Learning and Simulations Identify Promising New Cancer Drug Candidates

Chemists used machine learning and molecular modeling to discover potential drugs that inhibit an enzyme responsible for uncontrolled cell division, which could be a promising target for cancer treatment.

9/29/2023

Machine learning model identifies chemical features driving cancer drug localization, paving way for more targeted therapies

Machine learning has been used to predict where drugs will concentrate in cellular compartments called condensates, which can affect the efficacy and safety of the drugs, and may lead to improved drug design.

10/12/2023

AI Speeds Up Protein Design, Helping Researchers Develop New Compounds Faster

Artificial intelligence (AI) is being used to design synthetic proteins, greatly speeding up the process of drug development and protein design in scientific research.