Structure-Based <i>de Novo</i> Molecular Generator Combined with Artificial Intelligence and Docking Simulations

Ma, Biao; Terayama, Kei; Matsumoto, Shigeyuki; Isaka, Yuta; Sasakura, Yoko; Iwata, Hiroaki; Araki, Mitsugu; Okuno, Yasushi

doi:10.1021/acs.jcim.1c00679

Cited by 43 publications

(55 citation statements)

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“…3 Recently, deep learning has been applied towards more efficient methods of sampling chemical space such that it is possible to identify promising candidate molecules faster. Deep generative models using policy based reinforcement learning (RL) [4][5][6][7][8][9][10] , value based RL 11 , learning a molecular latent space 12 , and other methods including tree search 13 and genetic algorithms [14][15][16] have been proposed to generate molecules that possess a desired set of properties. In the policy based RL paradigm, an agent (a generative model) learns a policy (series of actions to take at given states) to generate molecules that maximize a reward which is typically computed based on a predefined reward function.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Improving de novo molecular design with curriculum learning

et al. 2022

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“…In this section, we demonstrate that simple curricula, utilizing a single Curriculum Objective can accelerate agent productivity and generate compounds that satisfy a docking constraint, i.e., predicted to retain experimentally validated interactions (see Methods for experiment hyperparameters). 6,7,[13][14][15] Simulating a real-world application where one must allocate limited computational resources, baseline RL and CL performances are compared, given a maximum number of permitted production epochs (300), i.e., epochs that involve docking, as these are relatively computationally demanding. For CL, Curriculum Objectives are first applied to guide the agent and the number of permitted curriculum epochs is not limited, as these are computationally inexpensive (see Table S2).…”

Section: Resultsmentioning

confidence: 99%

“…3 Recently, deep learning has been applied towards more efficient methods of sampling chemical space such that it is possible to identify promising candidate molecules faster. Deep generative models using policy-based reinforcement learning (RL) [4][5][6][7][8][9][10] , value based RL 11 , learning a molecular latent space 12 , and other methods including tree search 13 and genetic algorithms [14][15][16] have been proposed to generate molecules that possess a desired set of properties. In the policy-based RL paradigm, an agent (a generative model) learns a policy (series of actions to take at given states) to generate molecules that maximize a reward which is typically computed based on a pre-defined reward function.…”

Section: Introductionmentioning

confidence: 99%

Improving De Novo Molecular Design with Curriculum Learning

Guo

Fialková

Arango

et al. 2022

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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“…They showed that the method finds compounds with high affinity for a given target protein more efficiently than reinforcement-based algorithms. Ma et al suggested a structure-based de novo molecular generator approach, SBMolGen, by combining an RNN-based SMILES generator and a Monte Carlo tree search with docking simulations [ 25 ]. In the SBMolGen approach, a generated molecule is docked with a target protein, and the result is used to provide feedback to the generator to optimize the docking score.…”

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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Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations

Cited by 43 publications

References 50 publications

Improving de novo molecular design with curriculum learning

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

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