Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design

• URL: http://arxiv.org/abs/2305.16160v1
• Date: Wed, 10 May 2023 14:00:50 GMT
• Title: Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design
• Authors: Jeff Guo, Philippe Schwaller
• Abstract summary: Molecular generative models should learn to satisfy a desired objective under minimal oracle evaluations. We propose a novel algorithm called Augmented Memory that combines data augmentation with experience replay. We show that scores obtained from oracle calls can be reused to update the model multiple times.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal oracle evaluations (computational prediction or wet-lab experiment). This problem becomes more apparent when using oracles that can provide increased predictive accuracy but impose a significant cost. Consequently, these oracles cannot be directly optimized under a practical budget. Molecular generative models have shown remarkable sample efficiency when coupled with reinforcement learning, as demonstrated in the Practical Molecular Optimization (PMO) benchmark. Here, we propose a novel algorithm called Augmented Memory that combines data augmentation with experience replay. We show that scores obtained from oracle calls can be reused to update the model multiple times. We compare Augmented Memory to previously proposed algorithms and show significantly enhanced sample efficiency in an exploitation task and a drug discovery case study requiring both exploration and exploitation. Our method achieves a new state-of-the-art in the PMO benchmark which enforces a computational budget, outperforming the previous best performing method on 19/23 tasks.

Related papers

• Adv-KD: Adversarial Knowledge Distillation for Faster Diffusion Sampling [2.91204440475204]
  Diffusion Probabilistic Models (DPMs) have emerged as a powerful class of deep generative models. They rely on sequential denoising steps during sample generation. We propose a novel method that integrates denoising phases directly into the model's architecture.
  arXiv  Detail & Related papers  (2024-05-31T08:19:44Z)
• Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation [0.4037357056611557]
  Generative molecular design for drug discovery has recently achieved a wave of experimental validation. The most important factor for downstream success is whether an in silico oracle is well correlated with the desired end-point. We introduce Saturn, which leverages the Augmented Memory algorithm and demonstrates the first application of the Mamba architecture for generative molecular design.
  arXiv  Detail & Related papers  (2024-05-27T11:37:36Z)
• Retrosynthesis prediction enhanced by in-silico reaction data augmentation [66.5643280109899]
  We present RetroWISE, a framework that employs a base model inferred from real paired data to perform in-silico reaction generation and augmentation. On three benchmark datasets, RetroWISE achieves the best overall performance against state-of-the-art models.
  arXiv  Detail & Related papers  (2024-01-31T07:40:37Z)
• Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design [0.4769602527256662]
  Generative molecular design has moved from proof-of-concept to real-world applicability. Key challenges in explainability and sample efficiency present opportunities to enhance generative design. Beamion is generally applicable to any language-based molecular generative model.
  arXiv  Detail & Related papers  (2023-09-25T08:43:13Z)
• Value function estimation using conditional diffusion models for control [62.27184818047923]
  We propose a simple algorithm called Diffused Value Function (DVF) It learns a joint multi-step model of the environment-robot interaction dynamics using a diffusion model. We show how DVF can be used to efficiently capture the state visitation measure for multiple controllers.
  arXiv  Detail & Related papers  (2023-06-09T18:40:55Z)
• Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset [0.15420205433587747]
  We present a two-step HPO method as a strategic solution to curbing computational demands and wait times. We present our recent application of the two-step HPO method to the development of neural network emulators for aerosol activation.
  arXiv  Detail & Related papers  (2023-02-08T02:38:26Z)
• When to Update Your Model: Constrained Model-based Reinforcement Learning [50.74369835934703]
  We propose a novel and general theoretical scheme for a non-decreasing performance guarantee of model-based RL (MBRL) Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns.
  arXiv  Detail & Related papers  (2022-10-15T17:57:43Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• Missingness Augmentation: A General Approach for Improving Generative Imputation Models [20.245637164975594]
  We propose a novel data augmentation method called Missingness Augmentation (MisA) for generative imputation models. As a general augmentation technique, MisA can be easily integrated into generative imputation frameworks. Experimental results demonstrate that MisA significantly improves the performance of many recently proposed generative imputation models.
  arXiv  Detail & Related papers  (2021-07-31T08:51:46Z)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)
• Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning [109.74041512359476]
  We study a number of design decisions for the predictive model in visual MBRL algorithms. We find that a range of design decisions that are often considered crucial, such as the use of latent spaces, have little effect on task performance. We show how this phenomenon is related to exploration and how some of the lower-scoring models on standard benchmarks will perform the same as the best-performing models when trained on the same training data.
  arXiv  Detail & Related papers  (2020-12-08T18:03:21Z)

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.