Structure-Based De Novo 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:10.26434/chemrxiv.14371967

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…Therefore, the impact that AI‐based de novo design will have on very difficult targets, the area in which FBDD excels, remains to be seen. Nevertheless, this is a rapidly developing field and strategies that integrate structure‐based information to drive improvements in generation are of particular interest [132].…”

Section: Growing a Fragmentmentioning

confidence: 99%

Fragment‐based drug discovery—the importance of high‐quality molecule libraries

et al. 2022

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Fragment‐based drug discovery (FBDD) is now established as a complementary approach to high‐throughput screening (HTS). Contrary to HTS, where large libraries of drug‐like molecules are screened, FBDD screens involve smaller and less complex molecules which, despite a low affinity to protein targets, display more ‘atom‐efficient’ binding interactions than larger molecules. Fragment hits can, therefore, serve as a more efficient start point for subsequent optimisation, particularly for hard‐to‐drug targets. Since the number of possible molecules increases exponentially with molecular size, small fragment libraries allow for a proportionately greater coverage of their respective ‘chemical space’ compared with larger HTS libraries comprising larger molecules. However, good library design is essential to ensure optimal chemical and pharmacophore diversity, molecular complexity, and physicochemical characteristics. In this review, we describe our views on fragment library design, and on what constitutes a good fragment from a medicinal and computational chemistry perspective. We highlight emerging chemical and computational technologies in FBDD and discuss strategies for optimising fragment hits. The impact of novel FBDD approaches is already being felt, with the recent approval of the covalent KRASG12C inhibitor sotorasib highlighting the utility of FBDD against targets that were long considered undruggable.

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Section: Growing a Fragmentmentioning

confidence: 99%

Fragment‐based drug discovery—the importance of high‐quality molecule libraries

et al. 2022

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“…Ma et al created the Structure-Based de Novo Molecular Generator (SBMolGen) (Ma et al, 2021), another deep learning method for de novo drug design based on Monte Carlo tree search (MCTS) Browne et al, 2012) and docking simulations. Molecular generation in SBMolGen is done by ChemTS (Yang et al, 2017), andrDock(Ruiz-Carmona et al, 2014) is employed for docking generated compounds to the target.…”

Section: De Novo Drug Designmentioning

confidence: 99%

Applications of machine learning in computer-aided drug discovery

2022

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Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.

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“…One way is to perform on-the-fly structurebased evaluations and feed the results into the generation neural networks (NNs). For example, Okuno and co-workers proposed to perform docking calculations and select top compounds by the docking scores at each Monte Carlo tree search (MCTS) step in the generation process 19 . Roy and co-workers used instead a drug-target affinity (DTA) prediction model for on-the-fly evaluation 11 .…”

Section: Introductionmentioning

confidence: 99%

A Simple Way to Incorporate Target Structural Information in Molecular Generative Models

Zhang

Huang

2023

Preprint

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Deep learning generative models are now being applied in various fields including drug discovery. In this work, we propose a novel approach to include target 3D structural information in molecular generative models for structure-based drug design. The method combines a message-passing neural network model that predicts docking scores with a generative neural network model as its reward function to navigate the chemical space searching for molecules that bind favorably with a specific target. A key component of the method is to construct target-specific molecular sets for training to overcome potential transferability issues. Tests on eight target proteins showed a 100-fold increase in hit generation compared to conventional docking calculations, and the ability to generate molecules similar to approved drugs or known active ligands for specific targets without prior knowledge. This method provides a general and highly efficient solution for structure-based molecular generation.

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

Cited by 5 publications

References 5 publications

Fragment‐based drug discovery—the importance of high‐quality molecule libraries

Fragment‐based drug discovery—the importance of high‐quality molecule libraries

Applications of machine learning in computer-aided drug discovery

A Simple Way to Incorporate Target Structural Information in Molecular Generative Models

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