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Found 5 papers, 3 papers with code
On Machine Learning Approaches for Protein-Ligand Binding Affinity Prediction
Binding affinity optimization is crucial in early-stage drug discovery.
ACEGEN: Reinforcement learning of generative chemical agents for drug discovery
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.
TorchRL: A data-driven decision-making library for PyTorch
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 dealing with complex real-world data and environments.
Efficient Reinforcement Learning Experimentation in PyTorch
Abstract: Deep reinforcement learning (RL) has proved successful at solving challenging environments but often requires long training times and very many samples.
Integrating Distributed Architectures in Highly Modular RL Libraries
In this work, we explore design choices to allow agent composability both at a local and distributed level of execution.
