On Understanding Degradation Kinetics of Pharmaceutic Gelatin Matrices for Precision Medicine: A Deep Learning Approach

Gao, Li; Jiang, Hongjie; Fu, King-Sun; He, Weikai

doi:10.1109/bigdata47090.2019.9005665

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…In the interest of the reader, we also provide links to the various methods’ publicly available source code or webservers. The use of ML (more recently DL) methods is not limited solely to SBDD, and they have also been applied to all other areas of the drug discovery pipeline such as LBDD (Bahi and Batouche, 2018 ), lead optimization (de Souza Neto et al, 2020 ), and assessment of absorption (Shin et al, 2018 ), metabolism (Wang et al, 2020 ; Litsa et al, 2021 ), binding kinetics (De Benedetti and Fanelli, 2018 ; Mardt et al, 2018 ; Gao et al, 2019 ; Nunes-Alves et al, 2020 ; Feizpour et al, 2021 ; Obeid et al, 2021 ; Hoffmann et al, 2022 ), efficacy (Lin et al, 2018 ; Benning et al, 2020 ; Li et al, 2021 a ; Zhu et al, 2021 ) and toxicity properties (Ferreira and Andricopulo, 2019 ; Liu et al, 2019 ; Shi et al, 2019 ; Cáceres et al, 2020 ; Feinberg et al, 2020 ). While these studies are not covered in this review, interested readers are encouraged to review these recent works as well.…”

Section: Discussionmentioning

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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