Multi-objective Molecular Optimization for Opioid Use Disorder Treatment
Using Generative Network Complex
- URL: http://arxiv.org/abs/2306.07484v1
- Date: Tue, 13 Jun 2023 01:12:31 GMT
- Title: Multi-objective Molecular Optimization for Opioid Use Disorder Treatment
Using Generative Network Complex
- Authors: Hongsong Feng, Rui Wang, Chang-Guo Zhan, Guo-Wei Wei
- Abstract summary: Opioid Use Disorder (OUD) has emerged as a significant global health issue.
In this study, we propose a deep generative model that combines a differential equation (SDE)-based diffusion modeling with the latent space of a pretrained autoencoder model.
The molecular generator enables efficient generation of molecules that are effective on multiple targets.
- Score: 5.33208055504216
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Opioid Use Disorder (OUD) has emerged as a significant global public health
issue, with complex multifaceted conditions. Due to the lack of effective
treatment options for various conditions, there is a pressing need for the
discovery of new medications. In this study, we propose a deep generative model
that combines a stochastic differential equation (SDE)-based diffusion modeling
with the latent space of a pretrained autoencoder model. The molecular
generator enables efficient generation of molecules that are effective on
multiple targets, specifically the mu, kappa, and delta opioid receptors.
Furthermore, we assess the ADMET (absorption, distribution, metabolism,
excretion, and toxicity) properties of the generated molecules to identify
drug-like compounds. To enhance the pharmacokinetic properties of some lead
compounds, we employ a molecular optimization approach. We obtain a diverse set
of drug-like molecules. We construct binding affinity predictors by integrating
molecular fingerprints derived from autoencoder embeddings, transformer
embeddings, and topological Laplacians with advanced machine learning
algorithms. Further experimental studies are needed to evaluate the
pharmacological effects of these drug-like compounds for OUD treatment. Our
machine learning platform serves as a valuable tool in designing and optimizing
effective molecules for addressing OUD.
Related papers
- Data-Efficient Molecular Generation with Hierarchical Textual Inversion [48.816943690420224]
We introduce Hierarchical textual Inversion for Molecular generation (HI-Mol), a novel data-efficient molecular generation method.
HI-Mol is inspired by the importance of hierarchical information, e.g., both coarse- and fine-grained features, in understanding the molecule distribution.
Compared to the conventional textual inversion method in the image domain using a single-level token embedding, our multi-level token embeddings allow the model to effectively learn the underlying low-shot molecule distribution.
arXiv Detail & Related papers (2024-05-05T08:35:23Z) - 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) - Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-directed Molecular Generation [0.0]
Mol-AIR is a reinforcement learning-based framework using adaptive intrinsic rewards for goal-directed molecular generation.
In benchmark tests, Mol-AIR demonstrates superior performance over existing approaches in generating molecules with desired properties.
arXiv Detail & Related papers (2024-03-29T10:44:51Z) - 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) - 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) - 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) - 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) - Molecular Attributes Transfer from Non-Parallel Data [57.010952598634944]
We formulate molecular optimization as a style transfer problem and present a novel generative model that could automatically learn internal differences between two groups of non-parallel data.
Experiments on two molecular optimization tasks, toxicity modification and synthesizability improvement, demonstrate that our model significantly outperforms several state-of-the-art methods.
arXiv Detail & Related papers (2021-11-30T06:10:22Z) - Optimizing Molecules using Efficient Queries from Property Evaluations [66.66290256377376]
We propose QMO, a generic query-based molecule optimization framework.
QMO improves the desired properties of an input molecule based on efficient queries.
We show that QMO outperforms existing methods in the benchmark tasks of optimizing small organic molecules.
arXiv Detail & Related papers (2020-11-03T18:51:18Z) - MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization [51.00815310242277]
generative models and reinforcement learning approaches made initial success, but still face difficulties in simultaneously optimizing multiple drug properties.
We propose the MultI-constraint MOlecule SAmpling (MIMOSA) approach, a sampling framework to use input molecule as an initial guess and sample molecules from the target distribution.
arXiv Detail & Related papers (2020-10-05T20:18:42Z)
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.