LIMO: Latent Inceptionism for Targeted Molecule Generation

• URL: http://arxiv.org/abs/2206.09010v1
• Date: Fri, 17 Jun 2022 21:05:58 GMT
• Title: LIMO: Latent Inceptionism for Targeted Molecule Generation
• Authors: Peter Eckmann, Kunyang Sun, Bo Zhao, Mudong Feng, Michael K. Gilson, Rose Yu
• Abstract summary: We present Latent Inceptionism on Molecules (LIMO), which significantly accelerates molecule generation with an inceptionism-like technique. Comprehensive experiments show that LIMO performs competitively on benchmark tasks. One of our generated drug-like compounds has a predicted $K_D$ of $6 cdot 10-14$ M against the human estrogen receptor.
• Score: 14.391216237573369
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generation of drug-like molecules with high binding affinity to target proteins remains a difficult and resource-intensive task in drug discovery. Existing approaches primarily employ reinforcement learning, Markov sampling, or deep generative models guided by Gaussian processes, which can be prohibitively slow when generating molecules with high binding affinity calculated by computationally-expensive physics-based methods. We present Latent Inceptionism on Molecules (LIMO), which significantly accelerates molecule generation with an inceptionism-like technique. LIMO employs a variational autoencoder-generated latent space and property prediction by two neural networks in sequence to enable faster gradient-based reverse-optimization of molecular properties. Comprehensive experiments show that LIMO performs competitively on benchmark tasks and markedly outperforms state-of-the-art techniques on the novel task of generating drug-like compounds with high binding affinity, reaching nanomolar range against two protein targets. We corroborate these docking-based results with more accurate molecular dynamics-based calculations of absolute binding free energy and show that one of our generated drug-like compounds has a predicted $K_D$ (a measure of binding affinity) of $6 \cdot 10^{-14}$ M against the human estrogen receptor, well beyond the affinities of typical early-stage drug candidates and most FDA-approved drugs to their respective targets. Code is available at https://github.com/Rose-STL-Lab/LIMO.

Related papers

• Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization [147.7899503829411]
  AliDiff is a novel framework to align pretrained target diffusion models with preferred functional properties. It can generate molecules with state-of-the-art binding energies with up to -7.07 Avg. Vina Score.
  arXiv  Detail & Related papers  (2024-07-01T06:10:29Z)
• Guided Multi-objective Generative AI to Enhance Structure-based Drug Design [0.0]
  We describe IDOLpro, a generative chemistry AI combining diffusion with multi-objective optimization for structure-based drug design. IDOLpro produces with binding affinities over 10%-20% better than the next best state-of-the-art method on each test set. We show that IDOLpro can generate molecules for a range of important disease-related targets with better binding affinity and synthetic accessibility than any molecule found in the virtual screen.
  arXiv  Detail & Related papers  (2024-05-20T05:08:55Z)
• DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design [62.68420322996345]
  Existing structured-based drug design methods treat all ligand atoms equally. We propose a new diffusion model, DecompDiff, with decomposed priors over arms and scaffold. Our approach achieves state-of-the-art performance in generating high-affinity molecules.
  arXiv  Detail & Related papers  (2024-02-26T05:21:21Z)
• Quantum Computing-Enhanced Algorithm Unveils Novel Inhibitors for KRAS [10.732020360180773]
  We introduce a quantum-classical generative model that seamlessly integrates the power of quantum algorithms trained on a 16-qubit IBM quantum computer. Our work shows for the first time the use of a quantum-generative model to yield experimentally confirmed biological hits.
  arXiv  Detail & Related papers  (2024-02-13T04:19:06Z)
• Improving Molecular Properties Prediction Through Latent Space Fusion [9.912768918657354]
  We present a multi-view approach that combines latent spaces derived from state-of-the-art chemical models. Our approach relies on two pivotal elements: the embeddings derived from MHG-GNN, which represent molecular structures as graphs, and MoLFormer embeddings rooted in chemical language. We demonstrate the superior performance of our proposed multi-view approach compared to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-10-20T20:29:32Z)
• HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations [3.3934198248179026]
  Hyperdimensional Computing (HDC) is a proposed learning paradigm that is able to leverage low-precision binary vector arithmetic. We show that HDC-based inference methods are as much as 90 times more efficient than more complex representative machine learning methods.
  arXiv  Detail & Related papers  (2023-03-27T21:21:46Z)
• 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)
• Chemical-Reaction-Aware Molecule Representation Learning [88.79052749877334]
  We propose using chemical reactions to assist learning molecule representation. Our approach is proven effective to 1) keep the embedding space well-organized and 2) improve the generalization ability of molecule embeddings. Experimental results demonstrate that our method achieves state-of-the-art performance in a variety of downstream tasks.
  arXiv  Detail & Related papers  (2021-09-21T00:08:43Z)
• 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)
• CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models [74.58583689523999]
  We propose an end-to-end framework, named CogMol, for designing new drug-like small molecules targeting novel viral proteins. CogMol combines adaptive pre-training of a molecular SMILES Variational Autoencoder (VAE) and an efficient multi-attribute controlled sampling scheme. CogMol handles multi-constraint design of synthesizable, low-toxic, drug-like molecules with high target specificity and selectivity.
  arXiv  Detail & Related papers  (2020-04-02T18:17:20Z)

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.