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• Started with AI in Drug Discovery

Started with AI in Drug Discovery

• Speakers: Hadrien Mary (Datamol), Chence Shi and Zuobai Zhang (TorchDrug), Kexin Huang (TDC)
• Molecular Modeling and Drug Discovery (M2D2) talks: https://valence-discovery.github.io/M2D2-meetings/

Abstract

This talk features three open-source projects that are lowering the entrance barriers in AI for drug discovery and improving the speed at which researchers can develop new computational methods for the field.

• Datamol is a python library built on top of RDKit and aims to be as light as possible. The main features are: simple pythonic API, easy manipulation of molecular objects with good default options, built-in efficient parallelization, out-of-the-box support for modern IO operations using fsspec, easy pre-processing of molecular datasets for ML pipelines.
• TDC (Therapeutics Data Commons) is an open-science initiative with AI/ML-ready datasets and AI/ML tasks for therapeutics, spanning the discovery and development of safe and effective medicines. TDC provides an ecosystem of tools, libraries, leaderboards, and community resources, including data functions, strategies for systematic model evaluation, meaningful data splits, data processors, and molecule generation oracles. All resources are integrated via an open Python library.
• TorchDrug is designed to cover graph machine learning in drug discovery. It includes methods from graph neural networks, geometric deep learning, knowledge graphs, deep generative models, reinforcement learning and more. It provides a comprehensive and flexible interface to support rapid prototyping of drug discovery models in PyTorch.