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Life
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Role of Machine and Deep Learning in Drug Screening
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Role of Machine and Deep Learning in Drug Screening

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Pharmaceutical Science".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 3627

Special Issue Editors

Dr. Shahid Ali

E-Mail Website
Guest Editor
School of Cyberspace Security, Hainan University, Haikou, China
Interests: machine learning; drug screening; enzyme engineering; protein folding; MD simulations
Dr. Teng Zhou

E-Mail Website
Guest Editor
School of Cyberspace Security, Hainan University, Haikou, China
Interests: neurodegenerative disease diagnosis by molecular imaging; early diagnosis of Alzheimer's disease by driving behaviors; medical information processing; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The role of machine learning (ML) and deep learning (DL) in drug screening has emerged as a transformative force in addressing complex disease challenges. These technologies have proven especially effective in the discovery and optimization of therapeutic candidates (inhibitors) for high-priority diseases such as cancer, neurodegenerative disorders, infectious diseases, and metabolic conditions like diabetes. By leveraging large-scale biological and chemical datasets, ML and DL models are enabling precision in drug-target predictions, toxicity assessments, and virtual screening, significantly accelerating the pipeline for effective drug development.

This Special Issue aims to gather state-of-the-art research and reviews that demonstrate the targeted application of ML and DL in drug screening, focusing on disease-specific use cases. It seeks to highlight advances in computational methodologies, their integration with experimental studies, and their impact on addressing unmet medical needs.

Dr. Shahid Ali
Dr. Teng Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning in drug screening
  • deep learning in drug discovery
  • virtual screening
  • molecular docking and dynamics
  • personalized medicine

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Published Papers (2 papers)

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Research

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21 pages, 5177 KB  
Article
Identification of FDA-Approved Drugs as Potential Inhibitors of WEE2: Structure-Based Virtual Screening and Molecular Dynamics with Perspectives for Machine Learning-Assisted Prioritization
by Shahid Ali, Abdelbaset Mohamed Elasbali, Wael Alzahrani, Taj Mohammad, Md. Imtaiyaz Hassan and Teng Zhou
Life 2026, 16(2), 185; https://doi.org/10.3390/life16020185 - 23 Jan 2026
Cited by 1 | Viewed by 968
Abstract
Wee1-like protein kinase 2 (WEE2) is an oocyte-specific kinase that regulates meiotic arrest and fertilization. Its largely restricted expression in female germ cells and absence in somatic tissues make it a highly selective target for reproductive health interventions. Despite its central role in [...] Read more.
Wee1-like protein kinase 2 (WEE2) is an oocyte-specific kinase that regulates meiotic arrest and fertilization. Its largely restricted expression in female germ cells and absence in somatic tissues make it a highly selective target for reproductive health interventions. Despite its central role in human fertility, no clinically approved WEE2 modulator is available. In this study, we employed an integrated in silico approach that combines structure-based virtual screening, molecular dynamics (MD) simulations, and MM-PBSA free-energy calculations to identify repurposed drug candidates with potential WEE2 inhibitory activity. Screening of ~3800 DrugBank compounds against the WEE2 catalytic domain yielded ten high-affinity hits, from which Midostaurin and Nilotinib emerged as the most mechanistically relevant based on kinase-targeting properties and pharmacological profiles. Docking analyses revealed strong binding affinities (−11.5 and −11.3 kcal/mol) and interaction fingerprints highly similar to the reference inhibitor MK1775, including key contacts with hinge-region residues Val220, Tyr291, and Cys292. All-atom MD simulations for 300 ns demonstrated that both compounds induce stable protein–ligand complexes with minimal conformational drift, decreased residual flexibility, preserved compactness, and stable intramolecular hydrogen-bond networks. Principal component and free-energy landscape analyses further indicate restricted conformational sampling of WEE2 upon ligand binding, supporting ligand-induced stabilization of the catalytic domain. MM-PBSA calculations confirmed favorable binding free energies for Midostaurin (−18.78 ± 2.23 kJ/mol) and Nilotinib (−17.47 ± 2.95 kJ/mol), exceeding that of MK1775. To increase the translational prioritization of candidate hits, we place our structure-based pipeline in the context of modern machine learning (ML) and deep learning (DL)-enabled virtual screening workflows. ML/DL rescoring and graph-based molecular property predictors can rapidly re-rank docking hits and estimate absorption, distribution, metabolism, excretion, and toxicity (ADMET) liabilities before in vitro evaluation. Full article
(This article belongs to the Special Issue Role of Machine and Deep Learning in Drug Screening)
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Review

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26 pages, 2168 KB  
Review
Exploring TANK-Binding Kinase 1 in Amyotrophic Lateral Sclerosis: From Structural Mechanisms to Machine Learning-Guided Therapeutics
by Farah Anjum, Maram Jameel Hulbah, Anas Shamsi and Taj Mohammad
Life 2025, 15(11), 1665; https://doi.org/10.3390/life15111665 - 24 Oct 2025
Cited by 1 | Viewed by 1666
Abstract
TANK-binding kinase 1 (TBK1) has emerged as one of the most compelling genetic contributors to amyotrophic lateral sclerosis (ALS), with heterozygous loss-of-function and pathogenic missense variants identified in patients across the ALS–frontotemporal dementia (FTD) spectrum. TBK1 participates in various core cellular processes associated [...] Read more.
TANK-binding kinase 1 (TBK1) has emerged as one of the most compelling genetic contributors to amyotrophic lateral sclerosis (ALS), with heterozygous loss-of-function and pathogenic missense variants identified in patients across the ALS–frontotemporal dementia (FTD) spectrum. TBK1 participates in various core cellular processes associated with motor neuron vulnerability, including autophagy, mitophagy, and innate immune regulation, indicating that TBK1 is likely a key determinant of ALS pathogenesis. Structurally, TBK1 exhibits a trimodular organization comprising a kinase domain, a ubiquitin-like domain, and a scaffold/dimerization domain. Multiple experimentally resolved conformations and inhibitor-bound complexes provide a foundation for structure-guided therapeutic design. Here, we synthesize current genetic and mechanistic evidence linking TBK1 dysfunction to ALS, emphasizing its dual roles in autophagy and neuroinflammation. We also summarize advances in structure-based and AI-assisted drug discovery approaches targeting TBK1. Finally, we outline key translational challenges, including isoform selectivity, biomarker validation, and central nervous system (CNS) delivery, highlighting TBK1 as a promising yet complex therapeutic target in ALS. By integrating computational modeling, machine learning frameworks, and experimental pharmacology, future research may accelerate the translation of TBK1 modulators into clinically effective therapies. Full article
(This article belongs to the Special Issue Role of Machine and Deep Learning in Drug Screening)
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