16 citations · 21 across the 6 of their papers we have counts for
6 papers
Test-Time Training Scaling Laws for Chemical Exploration in Drug Design
Morgan Thomas, Albert Bou, Gianni De Fabritiis
Chemical Language Models (CLMs) leveraging reinforcement learning (RL) have shown promise in de novo molecular design, yet often suffer from mode collapse, limiting their explorati…
REINFORCE-ING Chemical Language Models for Drug Discovery
Morgan Thomas, Albert Bou, Jose Carlos Gómez-Tamayo +3
Chemical language models, combined with reinforcement learning (RL), have shown significant promise to efficiently traverse large chemical spaces for drug discovery. However, the p…
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…
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…
TorchRL: A data-driven decision-making library for PyTorch
Albert Bou, Matteo Bettini, Sebastian Dittert +5
PyTorch has ascended as a premier machine learning framework, yet it lacks a native and comprehensive library for decision and control tasks suitable for large development teams de…
Integrating Distributed Architectures in Highly Modular RL Libraries
Albert Bou, Sebastian Dittert, Gianni De Fabritiis
Advancing reinforcement learning (RL) requires tools that are flexible enough to easily prototype new methods while avoiding impractically slow experimental turnaround times. To ma…