Spectrum of deep learning algorithms in drug discovery

Piroozmand, Firoozeh; Mohammadipanah, Fatemeh; Sajedi, Hedieh

doi:10.1111/cbdd.13674

Cited by 15 publications

(6 citation statements)

References 133 publications

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“…Besides generating new chemical structures with favorable pharmacokinetics, ML methods are also used in pharmaceutical research and development for peptide design, compound activity prediction and for assisting scoring protein–ligand interaction (docking). ,− An example of the latter was proposed by Batra et al for efficiently identifying ligands that can potentially limit the host–virus interactions of SARS-CoV-2. Those authors designed a high-throughput strategy based on CompChem+ML that involved high-fidelity docking studies to find candidates displaying high-binding affinities.…”

Section: Selected Applications and Paths Toward Insightsmentioning

confidence: 99%

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

et al. 2021

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Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.

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Section: Selected Applications and Paths Toward Insightsmentioning

confidence: 99%

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

et al. 2021

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“…While ML algorithms like SVM have shown a high ratio of predicted known hits and lower false-positive rates, DL algorithms can improve prediction due to their dependable classification capabilities, robust feature extraction ability, and low generalization error. LigBuilder, ADAPT, PEP, SYNOPSIS, GANDI, and MEGA are among the de novo drug design tools that use the structure-based drug design technique (Piroozmand et al, 2020). ADMET prediction using deep learning methods has been demonstrated to be more accurate than predictions using classical ML approaches such as random forest algorithms.…”

Section: In Early Stages Of Drug Discoverymentioning

confidence: 99%

Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives

et al. 2022

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Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.

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“…Besides generating new chemical structures with favorable pharmacokinetics, ML methods are also used in pharmaceutical research and development for peptide design, compound activity prediction and for assisting scoring protein-ligand interaction (docking). 696,[702][703][704] An example of the latter was proposed by Batra et al 705 for efficiently identifying ligands that can potentially limit the host-virus interactions of SARS-CoV-2. Those authors designed a high-throughput strategy based on CompChem+ML that involved high-fidelity docking studies to find candidates displaying high-binding affinities.…”

Section: Drug Designmentioning

confidence: 99%

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Keith,

Vassilev-Galindo,

Cheng

et al. 2021

Preprint

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Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We then follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.

show abstract

Spectrum of deep learning algorithms in drug discovery

Cited by 15 publications

References 133 publications

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

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