Generative Active Learning for the Search of Small-molecule Protein Binders

• URL: http://arxiv.org/abs/2405.01616v1
• Date: Thu, 2 May 2024 16:39:21 GMT
• Title: Generative Active Learning for the Search of Small-molecule Protein Binders
• Authors: Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, SaiKrishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampášek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin Segler, Michael Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio,
• Abstract summary: We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small soluble molecules that inhibit the enzyme Epoxide Hydrolase 2 (sEH) LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH.
• Score: 87.106635995213
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH.

Related papers

• FARM: Functional Group-Aware Representations for Small Molecules [55.281754551202326]
  We introduce Functional Group-Aware Representations for Small Molecules (FARM) FARM is a foundation model designed to bridge the gap between SMILES, natural language, and molecular graphs. We rigorously evaluate FARM on the MoleculeNet dataset, where it achieves state-of-the-art performance on 10 out of 12 tasks.
  arXiv  Detail & Related papers  (2024-10-02T23:04:58Z)
• RGFN: Synthesizable Molecular Generation Using GFlowNets [51.33672611338754]
  We propose Reaction-GFlowNet, an extension of the GFlowNet framework that operates directly in the space of chemical reactions. RGFN allows out-of-the-box synthesizability while maintaining comparable quality of generated candidates. We demonstrate the effectiveness of the proposed approach across a range of oracle models, including pretrained proxy models and GPU-accelerated docking.
  arXiv  Detail & Related papers  (2024-06-01T13:11:11Z)
• 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)
• SILVR: Guided Diffusion for Molecule Generation [0.0]
  We introduce a machine-learning method for conditioning an existing generative model without retraining. The model allows the generation of new molecules that fit into a binding site of a protein based on fragment hits. We show that moderate SILVR rates make it possible to generate new molecules of similar shape to the original fragments.
  arXiv  Detail & Related papers  (2023-04-21T11:47:38Z)
• Machine learning for the prediction of safe and biologically active organophosphorus molecules [2.169755083801688]
  We propose a framework of Recurrent Neural Networks (RNN) with an attention model to sample the chemical space of organophosphorus molecules. The goal is to predict molecules with similar biological action modes as organophosphorus pesticides or chemical warfare agents yet less toxic to humans.
  arXiv  Detail & Related papers  (2023-02-21T19:12:35Z)
• 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)
• RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design [75.14290780116002]
  We train deep graph neural networks to approximate the outputs of a retrosynthesis planning software. Our approach finds molecules predicted to be more likely to be antibiotics while maintaining good drug-like properties and being easily synthesizable.
  arXiv  Detail & Related papers  (2020-11-25T22:04:16Z)
• 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.