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Artificial Intelligence in Drug Design: Molecular Aspects

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Pharmacology".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 185

Editor

Special Issue Information

Dear Colleagues,

Recent advancements in artificial intelligence and machine learning have significantly accelerated the process of drug discovery. By using large datasets encompassing protein structures, compound activities, and selectivity profiles, AI-driven techniques can discern complex patterns and relationships. The use of artificial intelligence for data analysis has advanced in-silico methods in pre-clinical research, saving time and cost compared to conventional methods. One of the recent examples of in-silico functional assays is GPCRVS, an AI-driven decision support system developed specifically to facilitate the web-based in-silico screening of GPCR drug candidates. Since the GPCRVS's first release in 2023, many AI-driven tools have been developed to enable massive data processing for drug design purposes. The main strength of such in-silico methods, compared to experimental approaches, is screening against many possible drug targets to minimize the risk of off-target effects, to enhance drug safety, and to increase the probability of clinical success.

The current issue is aimed at computational methods for drug discovery driven by artificial intelligence that enable the benefit from Big Data collected so far in various successful or even failed pharmaceutical campaigns. Detailed reviews comparing the usage of AI tools to discover new drugs are welcome. Research articles presenting new approaches to in-silico drug design are in the scope of this issue. Studies showing efficient integration of in-silico methods with experimental validation will receive a high acceptance rate. Applications of artificial intelligence and machine learning to medical imaging, offering unprecedented accuracy in disease detection and diagnostic support, are welcome. The current issue is not limited to G protein-coupled receptors, and articles presenting the usage of AI in drug discovery regarding other disease-associated signaling pathways, e.g., kinase signaling, will be accepted. Both intra- and intercellular signaling are within the scope of this issue, with emphasis on metastasis prevention and cell apoptosis induced in anticancer therapies.

Dr. Dorota Latek
Guest Editor

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • machine learning
  • drug design
  • medical imaging
  • apoptosis
  • metastasis
  • anticancer therapy

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Published Papers (1 paper)

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Research

28 pages, 2875 KB  
Article
Multi-Property De Novo Drug Design Using Deep Learning-Based Knowledge Distillation and Reinforcement Learning
by Liuying Wang, Zhao Lu, Lijuan Cui, Chang Liu, Yuting Qin, Shundan Feng, Dongxue Wang, Weixue Yin, Zheng Kang and Lei Cao
Int. J. Mol. Sci. 2026, 27(14), 6125; https://doi.org/10.3390/ijms27146125 - 8 Jul 2026
Abstract
Computer-aided de novo drug design has been widely explored for early-stage drug discovery, yet the multi-property optimization of novel molecules remains challenging. We aimed to develop a de novo drug design model to efficiently optimize multiple properties simultaneously. We developed a teacher–student-interaction deep [...] Read more.
Computer-aided de novo drug design has been widely explored for early-stage drug discovery, yet the multi-property optimization of novel molecules remains challenging. We aimed to develop a de novo drug design model to efficiently optimize multiple properties simultaneously. We developed a teacher–student-interaction deep learning model fine-tuned by reinforcement learning (TSItransRL) using bioactivity datasets (DRD2 and JNK3/GSK3β targets). A conditional transformer was pretrained as the teacher model to incorporate multi-property information. A vanilla transformer served as the student model and was subsequently optimized through interactive knowledge distillation and reinforcement learning. An evaluation was conducted using MOSES and conditional metrics on two tasks, specifically generating molecules with DRD2-targeting activity and generating molecules with dual JNK3/GSK3β-targeting activity, with the analyses including docking, the similarity ensemble approach (SEA), and scaffold novelty. TSItransRL achieved success rates of 98.36% and 98.90% for the DRD2 and JNK3/GSK3β tasks, respectively, with an internal diversity of 0.795, outperforming most baselines. The docking, SEA, scaffold, and ADMET analyses were used as exploratory in silico assessments to support the preliminary prioritization of selected generated molecules. TSItransRL provides an in silico framework for benchmark-level multi-property molecular generation and prioritization, combining interactive knowledge distillation with reinforcement learning to explore molecules that satisfy predefined predicted-activity, drug-likeness, and synthetic-accessibility criteria. The generated molecules should be regarded as computational candidates for a further medicinal-chemistry assessment, independent validation, and experimental testing rather than experimentally validated leads. Full article
(This article belongs to the Special Issue Artificial Intelligence in Drug Design: Molecular Aspects)
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