Building a next-generation toolbox for AI-powered drug discovery

Building a next-generation toolbox for AI-powered drug discovery

Brought to you by MilliporeSigma

Overview

There’s no question that artificial intelligence (AI) is now embedded in the drug discovery process at a growing number of companies. Machine learning, deep learning, and generative AI can help scientists generate structures for new drug candidates and explore protein-ligand binding before starting any experiments in a wet lab.

Use cases for AI in drug discovery are still evolving, and AI-powered drug ideation still must overcome many hurdles before it becomes commonplace and reliable. Here’s the latest on how AI supports drug discovery.

Key Objectives:

Machine learning (ML) and deep learning (DL) and methods for virtual screening of protein-ligand interactions
Combining ML or DL with physics-based modeling techniques, such as free energy perturbation calculations, to assess ligand-protein binding
Designing new molecules with generative AI
Incorporating predictive ADMET at early stages of drug design
Advancing algorithms with robust benchmarking and higher-quality training data

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