Here the authors combine a deep generative model with structure-based drug design and prospectively validate functionally active, nanomolar, A2A adenosine receptor ligands and solve their crystal stru...

AbstractA critical assessment of computational hit-finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-1...

If you want to know more about #FeNNix-Bio1, the first #machinelearning foundation model able to perform accurate - long timescale- condensed phase molecular simulations of biological systems at quant...

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Neural network potentials now offer robust alternatives to electronic structure and empirical force fields computations for the on-the-fly production of the potential energy surfaces required in atomi...

'First DREAM Target 2035 Drug Discovery Challenge' (Synapse ID: syn65660836) is a project on Synapse. Synapse is a platform for supporting scientific collaborations centered around shared biomedic...

The future of drug discovery may be artificial intelligence (AI), but its present is not. AI is in its infancy in the field. To help AI mature, developers need nonproprietary, open, large, high-qualit...

Thousands of 3D protein structures locked up in big-pharma vaults will be used to create a new AI tool that won’t be open to academics.

Registration is now open for the second annual Conscience Symposium on Open Drug Discovery! Join us at the Society for Arts and Technology in Montreal on April 7-8, 2025, for two days of insightful ta...

Scientific Data - The QCML dataset, Quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations

Computational tools for structure-based drug design (SBDD) are widely used in drug discovery and can provide valuable insights to advance projects in an efficient and cost-effective manner. However, d...

Making querying more flexible without using SMARTS

This analysis will surely lead to some spirited discussion and debate.

Advances in machine learning have transformed structural biology, enabling swift and accurate prediction of protein structure from sequence. However, challenges persist in capturing sidechain packing,...

Inference code for scalable emulation of protein equilibrium ensembles with generative deep learning - microsoft/bioemu

Deep learning has driven major breakthroughs in protein structure prediction, however the next critical advance is accurately predicting how proteins interact with other molecules, especially small mo...

This Perspective discusses recent strategies to expand the scope of targeted protein degradation (TPD) and the implications of unexpected convergence of diverse screening efforts on a small subset of ...

Simulating large molecular systems over long timescales requires force fields that are both accurate and efficient. In recent years, E(3) equivariant neural networks have lifted the tension between co...

Machine Learning in Drug Discovery Resources 2024. Contribute to PatWalters/resources_2025 development by creating an account on GitHub.

While deep learning has revolutionized computer-aided drug discovery, the AI community has predominantly focused on model innovation and placed less emphasis on establishing best benchmarking practice...