ACEGEN: Reinforcement learning of generative chemical agents for drug discovery

• URL: http://arxiv.org/abs/2405.04657v3
• Date: Mon, 22 Jul 2024 17:48:37 GMT
• Title: ACEGEN: Reinforcement learning of generative chemical agents for drug discovery
• Authors: Albert Bou, Morgan Thomas, Sebastian Dittert, Carles Navarro Ramírez, Maciej Majewski, Ye Wang, Shivam Patel, Gary Tresadern, Mazen Ahmad, Vincent Moens, Woody Sherman, Simone Sciabola, Gianni De Fabritiis,
• Abstract summary: ACEGEN is a comprehensive and streamlined toolkit for generative drug design. TorchRL is a modern RL library that offers thoroughly tested reusable components. We show examples of ACEGEN applied in multiple drug discovery case studies.
• Score: 4.966722586536789
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. However, striking a balance between capabilities, flexibility, reliability, and efficiency remains challenging due to the complexity of advanced RL algorithms and the significant reliance on specialized code. In this work, we introduce ACEGEN, a comprehensive and streamlined toolkit tailored for generative drug design, built using TorchRL, a modern RL library that offers thoroughly tested reusable components. We validate ACEGEN by benchmarking against other published generative modeling algorithms and show comparable or improved performance. We also show examples of ACEGEN applied in multiple drug discovery case studies. ACEGEN is accessible at \url{https://github.com/acellera/acegen-open} and available for use under the MIT license.

Related papers

• DrugImproverGPT: A Large Language Model for Drug Optimization with Fine-Tuning via Structured Policy Optimization [53.27954325490941]
  Finetuning a Large Language Model (LLM) is crucial for generating results towards specific objectives. This research introduces a novel reinforcement learning algorithm to finetune a drug optimization LLM-based generative model.
  arXiv  Detail & Related papers  (2025-02-11T04:00:21Z)
• Test-Time Training Scaling for Chemical Exploration in Drug Design [3.1406146587437904]
  We propose a new benchmark to discover dissimilar molecules that possess similar bioactivity. We show that a population of RL agents can solve the benchmark, while a single agent cannot. We also find that cooperative strategies are not significantly better than independent agents.
  arXiv  Detail & Related papers  (2025-01-31T14:11:10Z)
• Many-Shot In-Context Learning for Molecular Inverse Design [56.65345962071059]
  Large Language Models (LLMs) have demonstrated great performance in few-shot In-Context Learning (ICL) We develop a new semi-supervised learning method that overcomes the lack of experimental data available for many-shot ICL. As we show, the new method greatly improves upon existing ICL methods for molecular design while being accessible and easy to use for scientists.
  arXiv  Detail & Related papers  (2024-07-26T21:10:50Z)
• Optimized Drug Design using Multi-Objective Evolutionary Algorithms with SELFIES [1.124958340749622]
  We deploy multi-objective evolutionary algorithms, namely NSGA-II, NSGA-III, and MOEA/D, for this purpose. In addition to the QED and SA score, we optimize compounds using the GuacaMol benchmark multi-objective task sets.
  arXiv  Detail & Related papers  (2024-05-01T09:06:30Z)
• Latent Chemical Space Searching for Plug-in Multi-objective Molecule Generation [9.442146563809953]
  We develop a versatile 'plug-in' molecular generation model that incorporates objectives related to target affinity, drug-likeness, and synthesizability. We identify PSO-ENP as the optimal variant for multi-objective molecular generation and optimization.
  arXiv  Detail & Related papers  (2024-04-10T02:37:24Z)
• Molecular Generative Adversarial Network with Multi-Property Optimization [3.0001188337985236]
  Deep generative models, such as generative adversarial networks (GANs), have been employed for $denovo$ molecular generation in drug discovery. This study introduces a novel GAN based on actor-critic RL with instant and global rewards, called InstGAN, to generate molecules at the token-level with multi-property optimization.
  arXiv  Detail & Related papers  (2024-03-29T08:55:39Z)
• DrugAssist: A Large Language Model for Molecule Optimization [29.95488215594247]
  DrugAssist is an interactive molecule optimization model that performs optimization through human-machine dialogue. DrugAssist has achieved leading results in both single and multiple property optimization. We publicly release a large instruction-based dataset called MolOpt-Instructions for fine-tuning language models on molecule optimization tasks.
  arXiv  Detail & Related papers  (2023-12-28T10:46:56Z)
• De novo Drug Design using Reinforcement Learning with Multiple GPT Agents [16.508471997999496]
  MolRL-MGPT is a reinforcement learning algorithm with multiple GPT agents for drug molecular generation. Our algorithm has shown promising results on the GuacaMol benchmark and exhibits efficacy in designing inhibitors against SARS-CoV-2 protein targets.
  arXiv  Detail & Related papers  (2023-12-21T13:24:03Z)
• Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis [55.30328162764292]
  Chemist-X is a comprehensive AI agent that automates the reaction condition optimization (RCO) task in chemical synthesis. The agent uses retrieval-augmented generation (RAG) technology and AI-controlled wet-lab experiment executions. Results of our automatic wet-lab experiments, achieved by fully LLM-supervised end-to-end operation with no human in the lope, prove Chemist-X's ability in self-driving laboratories.
  arXiv  Detail & Related papers  (2023-11-16T01:21:33Z)
• Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning [82.93806087715507]
  Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. Deep learning models have emerged as an efficient way to discover synergistic combinations. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
  arXiv  Detail & Related papers  (2023-01-14T15:07:43Z)
• ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery [79.08833067391093]
  Real-world pharmaceutical datasets often exhibit highly imbalanced distribution. We introduce ImDrug, a benchmark with an open-source Python library which consists of 4 imbalance settings, 11 AI-ready datasets, 54 learning tasks and 16 baseline algorithms tailored for imbalanced learning. It provides an accessible and customizable testbed for problems and solutions spanning a broad spectrum of the drug discovery pipeline.
  arXiv  Detail & Related papers  (2022-09-16T13:35:57Z)
• Tailoring Molecules for Protein Pockets: a Transformer-based Generative Solution for Structured-based Drug Design [133.1268990638971]
  De novo drug design based on the structure of a target protein can provide novel drug candidates. We present a generative solution named TamGent that can directly generate candidate drugs from scratch for a given target.
  arXiv  Detail & Related papers  (2022-08-30T09:32:39Z)
• Retrieval-based Controllable Molecule Generation [63.44583084888342]
  We propose a new retrieval-based framework for controllable molecule generation. We use a small set of molecules to steer the pre-trained generative model towards synthesizing molecules that satisfy the given design criteria. Our approach is agnostic to the choice of generative models and requires no task-specific fine-tuning.
  arXiv  Detail & Related papers  (2022-08-23T17:01:16Z)
• Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning [75.95376096628135]
  We propose a novel forward synthesis framework powered by reinforcement learning (RL) for de novo drug design. In this setup, the agent learns to navigate through the immense synthetically accessible chemical space. We describe how the end-to-end training in this study represents an important paradigm in radically expanding the synthesizable chemical space.
  arXiv  Detail & Related papers  (2020-04-26T21:40:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.