126 citations · 295 across the 20 of their papers we have counts for
6 papers · 1 filter
On Machine Learning Approaches for Protein-Ligand Binding Affinity Prediction
Nikolai Schapin, Carles Navarro, Albert Bou +1
Binding affinity optimization is crucial in early-stage drug discovery. While numerous machine learning methods exist for predicting ligand potency, their comparative efficacy rema…
PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks
Nikolai Schapin, Maciej Majewski, Mariona Torrens-Fontanals +1
Small molecule protonation is an important part of the preparation of small molecules for many types of computational chemistry protocols. For this, a correct estimation of the pKa…
mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics
Antonio Mirarchi, Toni Giorgino, Gianni De Fabritiis
Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive data…
BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO
Sebastian Dittert, Vincent Moens, Gianni De Fabritiis
We present BricksRL, a platform designed to democratize access to robotics for reinforcement learning research and education. BricksRL facilitates the creation, design, and trainin…
ACEGEN: Reinforcement learning of generative chemical agents for drug discovery
Albert Bou, Morgan Thomas, Sebastian Dittert +10
In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. Howeve…
Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials
Francesc Sabanes Zariquiey, Raimondas Galvelis, Emilio Gallicchio +3
This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural ne…