Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.7
4.8
Journals
Pharmaceuticals
Special Issues
Artificial Intelligence-Assisted Drug Discovery
share announcement

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 May 2026 | Viewed by 18609

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, 1649-028 Lisbon, Portugal
Interests: medicinal chemistry; IA; molecular docking; QSAR; molecular dynamics; ADMET

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 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. 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

14 pages, 2210 KB  
Article
XGBPred-ACSM: A Hybrid Descriptor-Driven XGBoost Framework for Anticancer Small Molecule Prediction
by Priya Dharshini Balaji, Subathra Selvam, Anuradha Thiagarajan, Honglae Sohn and Thirumurthy Madhavan
Pharmaceuticals 2026, 19(4), 635; https://doi.org/10.3390/ph19040635 - 17 Apr 2026
Viewed by 294
Abstract
Background/Objectives: Cancer remains one of the leading global health burdens, mainly because of the lack of specificity and off-target toxicity associated with conventional therapeutic approaches. To move toward more efficient anticancer drug discovery, we have developed an advanced machine-learning-based architecture that allows [...] Read more.
Background/Objectives: Cancer remains one of the leading global health burdens, mainly because of the lack of specificity and off-target toxicity associated with conventional therapeutic approaches. To move toward more efficient anticancer drug discovery, we have developed an advanced machine-learning-based architecture that allows for predictive modeling of anticancer small molecules. Methods: A total of 3600 compounds with experimentally validated IC50 values were systematically processed to derive a comprehensive suite of molecular representations comprising 2D physicochemical descriptors, structural fingerprints, and hybrid descriptor sets generated via the Mordred and PaDEL frameworks. A total of six machine learning algorithms—Random Forest (RF), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Extra-Trees classifier (ET), Adaptive Boosting (AdaBoost), and Light Gradient Boosting Machine (LightGBM)—were trained and benchmarked via a rigorous model evaluation protocol incorporating 10-fold cross-validation along with multiple performance metrics. Ensemble voting strategies were also examined to assess potential performance. Result: Of all configurations, the XGB-Hybrid architecture emerged as the most robust and generalizable classifier with an AUC of 0.88 and accuracy of 79.11% on the independent test set. To ensure interpretability and mechanistic insight, SHAP-based feature analysis was conducted, by which feature contributions could be quantified and the molecular determinants most influential for anticancer activity discrimination were revealed. Altogether, the current study establishes an XGB-Hybrid framework as technically rigorous, interpretable, and high-performance predictive modeling with the ability to accelerate early-stage anticancer small molecule identification. Conclusions: The study has brought into focus the transformational effect of machine learning in modern computational oncology and rational drug design pipelines. Full article
(This article belongs to the Special Issue Artificial Intelligence-Assisted Drug Discovery)
►▼ Show Figures

Figure 1

34 pages, 7137 KB  
Article
NovelHTI: An Interpretable Pathway-Enhanced Framework for De Novo Target Prediction of Medicinal Herbs via Cross-Scale Heterogeneous Information Fusion
by Yuyam Cheung
Pharmaceuticals 2026, 19(3), 413; https://doi.org/10.3390/ph19030413 - 3 Mar 2026
Viewed by 706
Abstract
Background: The modernization of Traditional Chinese Medicine (TCM) is hindered by a “structure-blind” bottleneck: establishing molecular mechanisms for complex formulations with uncharacterized chemical constituents. Conventional computational screening fails in these scenarios due to a heavy reliance on pre-determined structures. We developed NovelHTI, an [...] Read more.
Background: The modernization of Traditional Chinese Medicine (TCM) is hindered by a “structure-blind” bottleneck: establishing molecular mechanisms for complex formulations with uncharacterized chemical constituents. Conventional computational screening fails in these scenarios due to a heavy reliance on pre-determined structures. We developed NovelHTI, an inductive graph-based framework designed to reverse-engineer protein targets directly from standardized clinical symptom profiles. Methods: NovelHTI implements a “Phenotype-to-Target” paradigm by integrating heterogeneous graph neural networks with systemic pathway constraints. Unlike traditional transductive models, NovelHTI leverages multi-view feature fusion of symptom semantics and biological pathways to enable de novo prediction for unseen herbs. The framework was evaluated across 698 herbs and 7854 targets, benchmarking against advanced GNNs (HAN) and non-graph classifiers (XGBoost) under strict cold-start and knowledge erosion simulations. Results: NovelHTI maintains high precision (>84%) and balanced performance (F1-score >77%), outperforming baselines by over 33% (ROC-AUC) in realistic imbalanced screening, where traditional models typically fail (AUC ≈ 0.51). Robustness analysis confirmed stable performance (>0.83 AUC) despite 30% structural data incompleteness. Notably, retrospective validation successfully “rediscovered” emerging mechanisms (e.g., the Artemisinin-GPX4 ferroptosis axis) elucidated in 2021–2024 literature, which were entirely latent in the training data. Conclusions: NovelHTI provides a robust computational prioritization filter that effectively bridges macroscopic phenotypes and microscopic pharmacology. By enabling mechanism-driven target de-risking, this framework optimizes resource allocation for downstream experimental validation and accelerates TCM-based drug discovery. Full article
(This article belongs to the Special Issue Artificial Intelligence-Assisted Drug Discovery)
►▼ Show Figures

Graphical abstract

13 pages, 2032 KB  
Article
OPLE: Drug Discovery Platform Combining 2D Similarity with AI to Predict Off-Target Liabilities
by Sarah E. Biehn, Juerg Lehmann, Christoph Mueller, Fabien Tillier and Carleton R. Sage
Pharmaceuticals 2026, 19(2), 228; https://doi.org/10.3390/ph19020228 - 28 Jan 2026
Viewed by 740
Abstract
Background/Objectives: An impediment to successful drug discovery is the potential for off-target liabilities to eliminate otherwise promising candidates. As the drug discovery process is time-consuming and expensive, the use of artificial intelligence (AI) methods such as machine learning (ML) has drastically increased. [...] Read more.
Background/Objectives: An impediment to successful drug discovery is the potential for off-target liabilities to eliminate otherwise promising candidates. As the drug discovery process is time-consuming and expensive, the use of artificial intelligence (AI) methods such as machine learning (ML) has drastically increased. It is invaluable to generate models that can quickly differentiate between successful and unsuccessful small-molecule drug candidates. Previous efforts established that molecular similarity could be used with other metrics to inform predictions of potential activity against a protein target. Similar methods were pursued here to combine similarity and machine learning for a collection of models called OPLE. Methods: Models were trained with proprietary and publicly available data to predict the likelihood of a given compound to be active against targets present in existing experimental SafetyScreen panels 18 and 44. Two-dimensional (2D) Tanimoto similarity from extended-connectivity fingerprints (ECFPs) and trained ML models were combined to obtain predictions. Results: Using all training data, a relationship between similarity and activity was established by fitting a probability assignment curve. Calibrated ML label assignment likelihoods were joined with the predictions from ECFP Tanimoto similarity to known active compounds using the belief theory formula, which maintains that activity prediction increases when both pieces of evidence support it. When assessing the performance of OPLE models for SafetyScreen 18 and 44 targets with external data from ChEMBL, more than 80% of the models had recall values greater than 0.8. This indicated favorable predictive ability to identify active molecules while limiting false negative predictions. Conclusions: Predicting and experimentally verifying safety liabilities is insightful at every stage of small-molecule drug discovery. This early detection tool can help project teams save resources that could be better deployed on series with no predicted or measured off-target liabilities. Full article
(This article belongs to the Special Issue Artificial Intelligence-Assisted Drug Discovery)
►▼ Show Figures

Graphical abstract

18 pages, 2563 KB  
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
Cited by 2 | Viewed by 2073
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)
►▼ Show Figures

Figure 1

Other

Jump to: Research

29 pages, 1320 KB  
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
Cited by 18 | Viewed by 13614
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop