5 papers
TerraBind: Fast and Accurate Binding Affinity Prediction through Coarse Structural Representations
Matteo Rossi, Ryan Pederson, Miles Wang-Henderson +8
We present TerraBind, a foundation model for protein-ligand structure and binding affinity prediction that achieves 26-fold faster inference than state-of-the-art methods while imp…
Boltz-ABFE: Free Energy Perturbation without Crystal Structures
Stephan Thaler, Zhiyi Wu, William G. Glass +3
Free energy perturbation (FEP) is considered the gold-standard simulation method for estimating small molecule binding affinity, a quantity of vital importance to drug discovery. T…
Implicit Delta Learning of High Fidelity Neural Network Potentials
Stephan Thaler, Cristian Gabellini, Nikhil Shenoy +1
Neural network potentials (NNPs) offer a fast and accurate alternative to ab-initio methods for molecular dynamics (MD) simulations but are hindered by the high cost of training da…
OpenQDC: Open Quantum Data Commons
Cristian Gabellini, Nikhil Shenoy, Stephan Thaler +5
Machine Learning Interatomic Potentials (MLIPs) are a highly promising alternative to force-fields for molecular dynamics (MD) simulations, offering precise and rapid energy and fo…
ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation
Majdi Hassan, Nikhil Shenoy, Jungyoon Lee +3
Predicting low-energy molecular conformations given a molecular graph is an important but challenging task in computational drug discovery. Existing state-of-the-art approaches eit…