Gianni De Fabritiis

J. Chem. Inf. Model., 2024

ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2024

AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics.

[BibT_eX]

[DOI]

CoRR, 2024

Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations.

[BibT_eX]

[DOI]

CoRR, 2024

On Machine Learning Approaches for Protein-Ligand Binding Affinity Prediction.

[BibT_eX]

[DOI]

CoRR, 2024

PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks.

[BibT_eX]

[DOI]

Nikolai Schapin

Mariona Torrens-Fontanals

CoRR, 2024

BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO.

[BibT_eX]

[DOI]

Sebastian Dittert

Vincent Moens

CoRR, 2024

On the Inclusion of Charge and Spin States in Cartesian Tensor Neural Network Potentials.

[BibT_eX]

[DOI]

CoRR, 2024

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations.

[BibT_eX]

[DOI]

CoRR, 2024

TorchRL: A data-driven decision-making library for PyTorch.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics.

[BibT_eX]

[DOI]

Raimondas Galvelis

Francesc Sabanés Zariquiey

J. Chem. Inf. Model., September, 2023

Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., April, 2023

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.

[BibT_eX]

[DOI]

CoRR, 2023

Machine Learning Small Molecule Properties in Drug Discovery.

[BibT_eX]

[DOI]

Nikolai Schapin

Carlos Arroniz

CoRR, 2023

Top-down machine learning of coarse-grained protein force-fields.

[BibT_eX]

[DOI]

Carles Navarro

CoRR, 2023

Binding-and-folding recognition of an intrinsically disordered protein using online learning molecular dynamics.

[BibT_eX]

[DOI]

Pablo Herrera-Nieto

Adrià Pérez

CoRR, 2023

TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials.

[BibT_eX]

[DOI]

Guillem Simeon

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2022

Machine Learning Coarse-Grained Potentials of Protein Thermodynamics.

[BibT_eX]

[DOI]

CoRR, 2022

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials.

[BibT_eX]

[DOI]

Benjamin P. Pritchard

Yuanqing Wang

Thomas E. Markland

CoRR, 2022

TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials.

[BibT_eX]

[DOI]

Philipp Thölke

CoRR, 2022

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics.

[BibT_eX]

[DOI]

Raimondas Galvelis

CoRR, 2022

Equivariant Transformers for Neural Network based Molecular Potentials.

[BibT_eX]

[DOI]

Philipp Thölke

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Guided Exploration with Proximal Policy Optimization using a Single Demonstration.

[BibT_eX]

[DOI]

Gabriele Libardi

Sebastian Dittert

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

SkeleDock: A Web Application for Scaffold Docking in PlayMolecule.

[BibT_eX]

[DOI]

Alberto Cuzzolin

J. Chem. Inf. Model., 2020

Generative Models for Molecular Design.

[BibT_eX]

[DOI]

Kenneth M. Merz Jr.

Guo-Wei Wei

J. Chem. Inf. Model., 2020

JCIM Special Issue on Generative Models for Molecular Design.

[BibT_eX]

[DOI]

Kenneth M. Merz Jr.

Guo-Wei Wei

J. Chem. Inf. Model., 2020

PlayMolecule CrypticScout: Predicting Protein Cryptic Sites Using Mixed-Solvent Molecular Simulations.

[BibT_eX]

[DOI]

Silvia Lovera

Zara A. Sands

J. Chem. Inf. Model., 2020

Small Molecule Modulation of Intrinsically Disordered Proteins Using Molecular Dynamics Simulations.

[BibT_eX]

[DOI]

Pablo Herrera-Nieto

Adrià Pérez

J. Chem. Inf. Model., 2020

TorchMD: A deep learning framework for molecular simulations.

[BibT_eX]

[DOI]

CoRR, 2020

Guided Exploration with Proximal Policy Optimization using a Single Demonstration.

[BibT_eX]

[DOI]

Gabriele Libardi

CoRR, 2020

NAPPO: Modular and scalable reinforcement learning in pytorch.

[BibT_eX]

[DOI]

Albert Bou

CoRR, 2020

2019

Shape-Based Generative Modeling for de Novo Drug Design.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2019

PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks.

[BibT_eX]

[DOI]

Davide Sabbadin

Alberto Cuzzolin

J. Chem. Inf. Model., 2019

A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2019

LigVoxel: inpainting binding pockets using 3D-convolutional neural networks.

[BibT_eX]

[DOI]

Miha Skalic

Bioinform., 2019

PlayMolecule BindScope: large scale CNN-based virtual screening on the web.

[BibT_eX]

[DOI]

Miha Skalic

Bioinform., 2019

2018

Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2018

KDEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks.

[BibT_eX]

[DOI]

Miha Skalic

J. Chem. Inf. Model., 2018

2017

PlayMolecule ProteinPrepare: A Web Application for Protein Preparation for Molecular Dynamics Simulations.

[BibT_eX]

[DOI]

Toni Giorgino

J. Chem. Inf. Model., July, 2017

Dimensionality reduction methods for molecular simulations.

[BibT_eX]

[DOI]

CoRR, 2017

DeepSite: protein-binding site predictor using 3D-convolutional neural networks.

[BibT_eX]

[DOI]

Stefan Doerr

Alexander S. Rose

Bioinform., 2017

2015

AceCloud: Molecular Dynamics Simulations in the Cloud.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2015

Insights from Fragment Hit Binding Assays by Molecular Simulations.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2015

2014

Reranking Docking Poses Using Molecular Simulations and Approximate Free Energy Methods.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2014

Kinetic Characterization of Fragment Binding in AmpC β-Lactamase by High-Throughput Molecular Simulations.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2014

2013

Computational Modeling of an Epidermal Growth Factor Receptor Single-Mutation Resistance to Cetuximab in Colorectal Cancer Treatment.

[BibT_eX]

[DOI]

Ignasi Buch

Noelia Ferruz

J. Chem. Inf. Model., 2013

2011

Swan: A tool for porting CUDA programs to OpenCL.

[BibT_eX]

[DOI]

Adrián López García de Lomana

Comput. Phys. Commun., 2011

2010

Induced Effects of Sodium Ions on Dopaminergic G-Protein Coupled Receptors.

[BibT_eX]

[DOI]

PLoS Comput. Biol., 2010

High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2010

Statistical Analysis of Global Connectivity and Activity Distributions in Cellular Networks.

[BibT_eX]

[DOI]

Qasim K. Beg

Jordi Villà-Freixa

J. Comput. Biol., 2010

Distributed computing as a virtual supercomputer: Tools to run and manage large-scale BOINC simulations.

[BibT_eX]

[DOI]

Toni Giorgino

Comput. Phys. Commun., 2010

2007

Performance of the Cell processor for biomolecular simulations.

[BibT_eX]

[DOI]