CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models

• URL: http://arxiv.org/abs/2004.01215v2
• Date: Wed, 24 Jun 2020 00:16:56 GMT
• Title: CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
• Authors: Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic
• Abstract summary: 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.
• Score: 74.58583689523999
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The novel nature of SARS-CoV-2 calls for the development of efficient de novo drug design approaches. In this study, we propose an end-to-end framework, named CogMol (Controlled Generation of Molecules), for designing new drug-like small molecules targeting novel viral proteins with high affinity and off-target selectivity. CogMol combines adaptive pre-training of a molecular SMILES Variational Autoencoder (VAE) and an efficient multi-attribute controlled sampling scheme that uses guidance from attribute predictors trained on latent features. To generate novel and optimal drug-like molecules for unseen viral targets, CogMol leverages a protein-molecule binding affinity predictor that is trained using SMILES VAE embeddings and protein sequence embeddings learned unsupervised from a large corpus. CogMol framework is applied to three SARS-CoV-2 target proteins: main protease, receptor-binding domain of the spike protein, and non-structural protein 9 replicase. The generated candidates are novel at both molecular and chemical scaffold levels when compared to the training data. CogMol also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations. Docking reveals favorable binding of generated molecules to the target protein structure, where 87-95 % of high affinity molecules showed docking free energy < -6 kcal/mol. When compared to approved drugs, the majority of designed compounds show low parent molecule and metabolite toxicity and high synthetic feasibility. In summary, CogMol handles multi-constraint design of synthesizable, low-toxic, drug-like molecules with high target specificity and selectivity, and does not need target-dependent fine-tuning of the framework or target structure information.

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)
• Multi-view deep learning based molecule design and structural optimization accelerates the SARS-CoV-2 inhibitor discovery [10.974317147338303]
  We propose MEDICO, a Multi-viEw Deep generative model for molecule generation, structural optimization, and SARS-CoV-2 Inhibitor disCOvery. We show that our MEDICO significantly outperforms the state-of-the-art methods in generating valid, unique, and novel molecules under benchmarking comparisons. Case study results on targeted molecule generation for the SARS-CoV-2 main protease (Mpro) show that by integrating molecule docking into our model as chemical priori, we successfully generate new small molecules with desired drug-like properties for the Mpro, potentially accelerating the de novo design of Covid
  arXiv  Detail & Related papers  (2022-12-03T08:21:13Z)
• State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
  We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
  arXiv  Detail & Related papers  (2022-09-30T01:46:38Z)
• 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)
• Widely Used and Fast De Novo Drug Design by a Protein Sequence-Based Reinforcement Learning Model [4.815696666006742]
  Structure-based de novo method can overcome the data scarcity of active by incorporating drug-target interaction into deep generative architectures. Here, we demonstrate a widely used and fast protein sequence-based reinforcement learning model for drug discovery. As a proof of concept, the RL model was utilized to design molecules for four targets.
  arXiv  Detail & Related papers  (2022-08-14T10:41:52Z)
• Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework [47.14853881703749]
  We demonstrate the broad utility of a single deep generative framework toward discovering novel drug-like inhibitor molecules. To perform target-aware design, the framework employs a target sequence-conditioned sampling of novel molecules from a generative model. The most potent spike RBD inhibitor also emerged as a rare non-covalent antiviral with broad-spectrum activity against several SARS-CoV-2 variants.
  arXiv  Detail & Related papers  (2022-04-19T17:59:46Z)
• AlphaFold Accelerates Artificial Intelligence Powered Drug Discovery: Efficient Discovery of a Novel Cyclin-dependent Kinase 20 (CDK20) Small Molecule Inhibitor [9.89420507558956]
  We successfully applied AlphaFold to identify a first-in-class hit molecule of a novel target without an experimental structure. We identified a small molecule hit compound for CDK20 with a Kd value of 8.9 +/- 1.6 uM within 30 days from target selection and after only 7 compounds. This is the first reported small molecule targeting CDK20 and more importantly, this work is the first demonstration of AlphaFold application in the hit identification process in early drug discovery.
  arXiv  Detail & Related papers  (2022-01-21T07:35:24Z)
• Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19 [11.853524110656991]
  Design of new drug compounds with target properties is a key area of research in generative modeling. We present a small drug molecule design pipeline based on graph-generative models and a comparison study of two state-of-the-art graph generative models for designing COVID-19 targeted drug candidates.
  arXiv  Detail & Related papers  (2021-02-09T17:49:26Z)
• PaccMann$^{RL}$ on SARS-CoV-2: Designing antiviral candidates with conditional generative models [2.0750380105212116]
  With the fast development of COVID-19 into a global pandemic, scientists around the globe are desperately searching for effective antiviral therapeutic agents. We propose a deep learning framework for conditional de novo design of antiviral candidate drugs tailored against given protein targets.
  arXiv  Detail & Related papers  (2020-05-27T11:30:15Z)

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.