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Pharmaceuticals
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Artificial Intelligence-Assisted Drug Discovery
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Artificial Intelligence-Assisted Drug Discovery

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: 25 July 2025 | Viewed by 1788

Special Issue Editor

Prof. Dr. Aldo Sena De Oliveira

E-Mail Website
Guest Editor
Gulbenkian Institute for Molecular Medicine (GIMM), Faculty of Medicine, University of Lisbon, Edifício Egas Moniz,1649-028 Lisbon, Portugal
Interests: computational chemistry; drug discovery; artificial intelligence; medicinal chemistry; neurodegenerative diseases

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) has emerged as a transformative tool in drug discovery, enabling researchers to address long-standing challenges and accelerate the development of novel therapeutic agents. AI technologies, such as machine learning, deep learning, and natural language processing, are reshaping key aspects of drug discovery, including virtual screening, de novo molecular design, the prediction of ADMET properties, and the identification of druggable targets. These methodologies promise to enhance precision, reduce costs, and significantly shorten the timelines for bringing effective drugs to market.

This Special Issue, "Artificial Intelligence-Assisted Drug Discovery", will explore the latest advancements and applications of AI in pharmaceutical research. We encourage submissions that discuss innovative AI-driven approaches in medicinal chemistry, structure-based drug design, and pharmacological profiling. Contributions focusing on multi-target drug design, drug repurposing, and the discovery of treatments for neglected diseases are particularly welcome.

We also seek studies that evaluate the integration of AI with traditional experimental methods, such as high-throughput screening, and explore the ethical considerations, limitations, and future directions of AI in drug development. This Special Issue aims to provide a comprehensive overview of how AI is transforming drug discovery and inspiring interdisciplinary collaboration to address unmet medical needs.

We look forward to your valuable contribution.

Prof. Dr. Aldo Sena De Oliveira
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Pharmaceuticals 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 2900 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

  • artificial intelligence
  • drug discovery
  • machine learning
  • medicinal chemistry
  • computational chemistry
  • virtual screening
  • ADMET predictions
  • de novo drug design
  • multi-target ligands
  • drug repositioning

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Published Papers (2 papers)

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18 pages, 2563 KiB  
Article
PLASMOpred: A Machine Learning-Based Web Application for Predicting Antimalarial Small Molecules Targeting the Apical Membrane Antigen 1–Rhoptry Neck Protein 2 Invasion Complex
by Eugene Lamptey, Jessica Oparebea, Gabriel Anyaele, Belinda Ofosu, George Hanson, Patrick O. Sakyi, Odame Agyapong, Dominic S. Y. Amuzu, Whelton A. Miller III, Samuel K. Kwofie and Henrietta Esi Mensah-Brown
Pharmaceuticals 2025, 18(6), 776; https://doi.org/10.3390/ph18060776 - 23 May 2025
Viewed by 789
Abstract
Objective: Falciparum malaria is a major global health concern, affecting more than half of the world’s population and causing over half a million deaths annually. Red cell invasion is a crucial step in the parasite’s life cycle, where the parasite invade human erythrocytes [...] Read more.
Objective: Falciparum malaria is a major global health concern, affecting more than half of the world’s population and causing over half a million deaths annually. Red cell invasion is a crucial step in the parasite’s life cycle, where the parasite invade human erythrocytes to sustain infection and ensure survival. Two parasite proteins, Apical Membrane Antigen 1 (AMA-1) and Rhoptry Neck Protein 2 (RON2), are involved in tight junction formation, which is an essential step in parasite invasion of the red blood cell. Targeting the AMA-1 and RON2 interaction with inhibitors halts the formation of the tight junction, thereby preventing parasite invasion, which is detrimental to parasite survival. This study leverages machine learning (ML) to predict potential small molecule inhibitors of the AMA-1–RON2 interaction, providing putative antimalaria compounds for further chemotherapeutic exploration. Method: Data was retrieved from the PubChem database (AID 720542), comprising 364,447 inhibitors and non-inhibitors of the AMA-1–RON2 interaction. The data was processed by computing Morgan fingerprints and divided into training and testing with an 80:20 ratio, and the classes in the training data were balanced using the Synthetic Minority Oversampling Technique. Five ML models developed comprised Random Forest (RF), Gradient Boost Machines (GBMs), CatBoost (CB), AdaBoost (AB) and Support Vector Machine (SVM). The performances of the models were evaluated using accuracy, F1 score, and receiver operating characteristic—area under the curve (ROC-AUC) and validated using held-out data and a y-randomization test. An applicability domain analysis was carried out using the Tanimoto distance with a threshold set at 0.04 to ascertain the sample space where the models predict with confidence. Results: The GBMs model emerged as the best, achieving 89% accuracy and a ROC-AUC of 92%. CB and RF had accuracies of 88% and 87%, and ROC-AUC scores of 93% and 91%, respectively. Conclusions: Experimentally validated inhibitors of the AMA-1–RON2 interaction could serve as starting blocks for the next-generation antimalarial drugs. The models were deployed as a web-based application, known as PLASMOpred. Full article
(This article belongs to the Special Issue Artificial Intelligence-Assisted Drug Discovery)
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29 pages, 1320 KiB  
Systematic Review
From Lab to Clinic: How Artificial Intelligence (AI) Is Reshaping Drug Discovery Timelines and Industry Outcomes
by Doni Dermawan and Nasser Alotaiq
Pharmaceuticals 2025, 18(7), 981; https://doi.org/10.3390/ph18070981 - 30 Jun 2025
Viewed by 589
Abstract
Background/Objectives: Artificial intelligence (AI) is transforming drug discovery and development by enhancing the speed and precision of identifying drug candidates and optimizing their efficacy. This review evaluates the application of AI in various stages of drug discovery, from hit identification to lead optimization, [...] Read more.
Background/Objectives: Artificial intelligence (AI) is transforming drug discovery and development by enhancing the speed and precision of identifying drug candidates and optimizing their efficacy. This review evaluates the application of AI in various stages of drug discovery, from hit identification to lead optimization, and its impact on clinical outcomes. The objective is to provide insights into the role of AI across therapeutic areas and assess its contributions to improving clinical trial efficiency and pharmaceutical outcomes. Methods: A systematic review followed PRISMA guidelines to analyze studies published between 2015 and 2025, focusing on AI in drug discovery and development. A comprehensive search was performed across multiple databases to identify studies employing AI techniques. The studies were categorized based on AI methods, clinical phase, and therapeutic area. The percentages of AI methods used, clinical phase stages, and the therapeutic regions were analyzed to identify trends. Results: AI methods included machine learning (ML) at 40.9%, molecular modeling and simulation (MMS) at 20.7%, and deep learning (DL) at 10.3%. Oncology accounted for the majority of studies (72.8%), followed by dermatology (5.8%) and neurology (5.2%). In clinical phases, 39.3% of studies were in the preclinical stage, 23.1% in Clinical Phase I, and 11.0% in the transitional phase. Clinical outcome reporting was observed in 45% of studies, with 97% reporting industry partnerships. Conclusions: AI significantly enhances drug discovery and development, improving drug efficacy and clinical trial outcomes. Future work should focus on expanding AI applications into underrepresented therapeutic areas and refining models to handle complex biological systems. Full article
(This article belongs to the Special Issue Artificial Intelligence-Assisted Drug Discovery)
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