DockStream: A Docking Wrapper to Enhance De Novo Molecular Design

Guo, Jeff; Janet, Jon Paul; Bauer, Matthias R.; Nittinger, Eva; Giblin, Kathryn A.; Papadopoulos, Kostas; Voronov, Alexey; Patronov, Atanas; Engkvist, Ola; Margreitter, Christian

doi:10.33774/chemrxiv-2021-qvhml

Cited by 5 publications

(2 citation statements)

References 59 publications

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“…37 Ligand preparation and docking was performed using DockStream, which is integrated with REINVENT, facilitating parallelization over numerous CPU cores. 38 3D coordinates for all agent sampled compounds from the baseline RL and CL experiments were generated using LigPrep. Default parameters were used except for the pH tolerance range set to 7.0 ± 1.0 with Epik and a maximum of two stereoisomers kept per compound.…”

Section: Methodsmentioning

confidence: 99%

Improving de novo molecular design with curriculum learning

et al. 2022

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Reinforcement learning (RL) is a powerful paradigm that has gained popularity across multiple domains.However, applying RL may come at a cost of multiple interactions between the agent and the environment. This cost can be especially pronounced when the single feedback from the environment is slow or computationally expensive, causing extensive periods of nonproductivity. Curriculum learning (CL) provides a suitable alternative by arranging a sequence of tasks of increasing complexity with the aim of reducing the overall cost of learning. Here, we demonstrate the application of CL for drug discovery. We implement CL in the de novo design platform, REINVENT, and apply it on illustrative de novo molecular design problems of different complexity. The results show both accelerated learning and a positive impact on the quality of the output when compared to standard policy based RL. To our knowledge, this is the first application of CL for the purposes of de novo molecular design. The code is freely available at https://github.com/MolecularAI/Reinvent.

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Section: Methodsmentioning

confidence: 99%

Improving de novo molecular design with curriculum learning

et al. 2022

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“…In addition to virtual screening, attempts have been made to generate molecules by directly optimizing a docking score [ 19 , 20 , 21 , 22 ]. Recently, Boitreaud et al [ 23 ] suggested the OptiMol approach based on binding energy optimization for drug design using a generative model and docking using adaptive sampling (CbAS) [ 24 ] to maximize the objective function.…”

Section: Introductionmentioning

confidence: 99%

V-Dock: Fast Generation of Novel Drug-like Molecules Using Machine-Learning-Based Docking Score and Molecular Optimization

Choi

Lee

2021

IJMS

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We propose a computational workflow to design novel drug-like molecules by combining the global optimization of molecular properties and protein-ligand docking with machine learning. However, most existing methods depend heavily on experimental data, and many targets do not have sufficient data to train reliable activity prediction models. To overcome this limitation, protein-ligand docking calculations must be performed using the limited data available. Such docking calculations during molecular generation require considerable computational time, preventing extensive exploration of the chemical space. To address this problem, we trained a machine-learning-based model that predicted the docking energy using SMILES to accelerate the molecular generation process. Docking scores could be accurately predicted using only a SMILES string. We combined this docking score prediction model with the global molecular property optimization approach, MolFinder, to find novel molecules exhibiting the desired properties with high values of predicted docking scores. We named this design approach V-dock. Using V-dock, we efficiently generated many novel molecules with high docking scores for a target protein, a similarity to the reference molecule, and desirable drug-like and bespoke properties, such as QED. The predicted docking scores of the generated molecules were verified by correlating them with the actual docking scores.

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Improving De Novo Molecular Design with Curriculum Learning

Guo

Fialková

Arango

et al. 2021

Preprint

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Reinforcement learning (RL) is a powerful paradigm that has gained popularity across multiple domains. However, applying RL may come at a cost of multiple interactions between the agent and the environment. This cost can be especially pronounced when the single feedback from the environment is slow or computationally expensive, causing extensive periods of nonproductivity. Curriculum learning (CL) provides a suitable alternative by arranging a sequence of tasks of increasing complexity with the aim of reducing the overall cost of learning. Here, we demonstrate the application of CL for drug discovery. We implement CL in the de novo design platform, REINVENT, and apply it on illustrative de novo molecular design problems of different complexity. The results show both accelerated learning and a positive impact on the quality of the output when compared to standard policy based RL. To our knowledge, this is the first application of CL for the purposes of de novo molecular design. The code is freely available at https://github.com/MolecularAI/Reinvent.

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DockStream: A Docking Wrapper to Enhance De Novo Molecular Design

Cited by 5 publications

References 59 publications

Improving de novo molecular design with curriculum learning

Improving de novo molecular design with curriculum learning

V-Dock: Fast Generation of Novel Drug-like Molecules Using Machine-Learning-Based Docking Score and Molecular Optimization

Improving De Novo Molecular Design with Curriculum Learning

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