Materials Discovery with Extreme Properties via Reinforcement Learning-Guided Combinatorial Chemistry
- URL: http://arxiv.org/abs/2303.11833v2
- Date: Tue, 7 May 2024 15:07:34 GMT
- Title: Materials Discovery with Extreme Properties via Reinforcement Learning-Guided Combinatorial Chemistry
- Authors: Hyunseung Kim, Haeyeon Choi, Dongju Kang, Won Bo Lee, Jonggeol Na,
- Abstract summary: Rule-based molecular designer driven by trained policy for selecting subsequent molecular fragments to get a target molecule.
In an experiment aimed at discovering molecules that hit seven extreme target properties, our model discovered 1,315 of all target-hitting molecules.
It has been confirmed that every molecule generated under the binding rules of molecular fragments is 100% chemically valid.
- Score: 0.23301643766310373
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The goal of most materials discovery is to discover materials that are superior to those currently known. Fundamentally, this is close to extrapolation, which is a weak point for most machine learning models that learn the probability distribution of data. Herein, we develop reinforcement learning-guided combinatorial chemistry, which is a rule-based molecular designer driven by trained policy for selecting subsequent molecular fragments to get a target molecule. Since our model has the potential to generate all possible molecular structures that can be obtained from combinations of molecular fragments, unknown molecules with superior properties can be discovered. We theoretically and empirically demonstrate that our model is more suitable for discovering better compounds than probability distribution-learning models. In an experiment aimed at discovering molecules that hit seven extreme target properties, our model discovered 1,315 of all target-hitting molecules and 7,629 of five target-hitting molecules out of 100,000 trials, whereas the probability distribution-learning models failed. Moreover, it has been confirmed that every molecule generated under the binding rules of molecular fragments is 100% chemically valid. To illustrate the performance in actual problems, we also demonstrate that our models work well on two practical applications: discovering protein docking molecules and HIV inhibitors.
Related papers
- STRIDE: Structure-guided Generation for Inverse Design of Molecules [0.24578723416255752]
$textbfSTRIDE$ is a generative molecule workflow that generates novel molecules with an unconditional generative model guided by known molecules without any retraining.
Our generated molecules have on average 21.7% lower synthetic accessibility scores and also reduce ionization potential by 5.9% of generated molecules via guiding.
arXiv Detail & Related papers (2023-11-06T08:22:35Z) - Diffusing on Two Levels and Optimizing for Multiple Properties: A Novel
Approach to Generating Molecules with Desirable Properties [33.2976176283611]
We present a novel approach to generating molecules with desirable properties, which expands the diffusion model framework with multiple innovative designs.
To get desirable molecular fragments, we develop a novel electronic effect based fragmentation method.
We show that the molecules generated by our proposed method have better validity, uniqueness, novelty, Fr'echet ChemNet Distance (FCD), QED, and PlogP than those generated by current SOTA models.
arXiv Detail & Related papers (2023-10-05T11:43:21Z) - Molecule Design by Latent Space Energy-Based Modeling and Gradual
Distribution Shifting [53.44684898432997]
Generation of molecules with desired chemical and biological properties is critical for drug discovery.
We propose a probabilistic generative model to capture the joint distribution of molecules and their properties.
Our method achieves very strong performances on various molecule design tasks.
arXiv Detail & Related papers (2023-06-09T03:04:21Z) - Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular
Property Prediction [53.06671763877109]
We develop molecular embeddings that encode complex molecular characteristics to improve the performance of few-shot molecular property prediction.
Our approach leverages large amounts of synthetic data, namely the results of molecular docking calculations.
On multiple molecular property prediction benchmarks, training from the embedding space substantially improves Multi-Task, MAML, and Prototypical Network few-shot learning performance.
arXiv Detail & Related papers (2023-02-04T01:32:40Z) - A Molecular Multimodal Foundation Model Associating Molecule Graphs with
Natural Language [63.60376252491507]
We propose a molecular multimodal foundation model which is pretrained from molecular graphs and their semantically related textual data.
We believe that our model would have a broad impact on AI-empowered fields across disciplines such as biology, chemistry, materials, environment, and medicine.
arXiv Detail & Related papers (2022-09-12T00:56:57Z) - Exploring Chemical Space with Score-based Out-of-distribution Generation [57.15855198512551]
We propose a score-based diffusion scheme that incorporates out-of-distribution control in the generative differential equation (SDE)
Since some novel molecules may not meet the basic requirements of real-world drugs, MOOD performs conditional generation by utilizing the gradients from a property predictor.
We experimentally validate that MOOD is able to explore the chemical space beyond the training distribution, generating molecules that outscore ones found with existing methods, and even the top 0.01% of the original training pool.
arXiv Detail & Related papers (2022-06-06T06:17:11Z) - Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning [68.8204255655161]
We introduce a novel framework for scalable 3D design that uses a hierarchical agent to build molecules.
In a variety of experiments, we show that our agent, guided only by energy considerations, can efficiently learn to produce molecules with over 100 atoms.
arXiv Detail & Related papers (2022-02-01T18:54:24Z) - Fragment-based molecular generative model with high generalization
ability and synthetic accessibility [0.0]
We propose a fragment-based molecular generative model which designs new molecules with target properties.
A key feature of our model is a high generalization ability in terms of property control and fragment types.
We show that the model can generate molecules with the simultaneous control of multiple target properties at a high success rate.
arXiv Detail & Related papers (2021-11-25T04:44:37Z) - Flexible dual-branched message passing neural network for quantum
mechanical property prediction with molecular conformation [16.08677447593939]
We propose a dual-branched neural network for molecular property prediction based on message-passing framework.
Our model learns heterogeneous molecular features with different scales, which are trained flexibly according to each prediction target.
arXiv Detail & Related papers (2021-06-14T10:00:39Z) - Advanced Graph and Sequence Neural Networks for Molecular Property
Prediction and Drug Discovery [53.00288162642151]
We develop MoleculeKit, a suite of comprehensive machine learning tools spanning different computational models and molecular representations.
Built on these representations, MoleculeKit includes both deep learning and traditional machine learning methods for graph and sequence data.
Results on both online and offline antibiotics discovery and molecular property prediction tasks show that MoleculeKit achieves consistent improvements over prior methods.
arXiv Detail & Related papers (2020-12-02T02:09:31Z)
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.