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Molecular Computer Science and Artificial Intelligence for Drug Discovery
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Molecular Computer Science and Artificial Intelligence for Drug Discovery

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: 20 March 2025 | Viewed by 11459

Special Issue Editor

Prof. Dr. Enkelejda Miho

E-Mail Website
Guest Editor
Hochschule für Life Sciences FHNW, Muttenz, Switzerland
Interests: artificial intelligence; computer science; drug discovery; personalized medicine

Special Issue Information

Dear Colleagues,

We are delighted to announce an upcoming special issue focused on the transformative role of computer science and Artificial Intelligence (AI) in the field of drug discovery. We invite you to contribute your expertise and insights to this exciting publication.

Computer science and AI techniques have revolutionized the way we approach drug discovery, offering innovative solutions to overcome traditional challenges. This special issue aims to explore the latest advancements in and future prospects of utilizing computer science and AI for accelerating the discovery of novel drugs, optimizing the drug discovery process and assays, and improving developability.

We encourage submissions that cover topics of AI applied to drug discovery, including technologies such as large language models and quantum computing:

  • Computational modeling and simulation in drug discovery;
  • Machine learning and deep learning approaches for drug target identification and drug design, repurposing, and hit-to-lead optimization;
  • Large language models applied to network pharmacology, pharmacogenomics, and systems medicine for understanding drug interactions and mechanisms of action;
  • Virtual screening and rational drug design using in silico methods;
  • Activity, dose, toxicity prediction of biologics and small molecules;
  • Computational investigation of developability characteristics;
  • Quantum computing for enhanced parameter space search in drug discovery.

Join us in shaping the future of drug discovery by sharing your research, perspectives, and innovations in this dynamic field. We welcome original research articles, reviews, and perspectives that shed light on the applications and challenges of computer science and AI in drug discovery.

We look forward to receiving your contributions and collaborating on this exciting special issue.

Prof. Dr. Enkelejda Miho
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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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
  • computer science
  • drug discovery
  • computational modeling
  • drug target identification
  • drug design
  • machine learning
  • deep learning
  • drug interactions

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Published Papers (5 papers)

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Research

17 pages, 4993 KiB  
Article
NFSA-DTI: A Novel Drug–Target Interaction Prediction Model Using Neural Fingerprint and Self-Attention Mechanism
by Feiyang Liu, Huang Xu, Peng Cui, Shuo Li, Hongbo Wang and Ziye Wu
Int. J. Mol. Sci. 2024, 25(21), 11818; https://doi.org/10.3390/ijms252111818 - 3 Nov 2024
Viewed by 899
Abstract
Existing deep learning methods have shown outstanding performance in predicting drug–target interactions. However, they still have limitations: (1) the over-reliance on locally extracted features by some single encoders, with insufficient consideration of global features, and (2) the inadequate modeling and learning of local [...] Read more.
Existing deep learning methods have shown outstanding performance in predicting drug–target interactions. However, they still have limitations: (1) the over-reliance on locally extracted features by some single encoders, with insufficient consideration of global features, and (2) the inadequate modeling and learning of local crucial interaction sites in drug–target interaction pairs. In this study, we propose a novel drug–target interaction prediction model called the Neural Fingerprint and Self-Attention Mechanism (NFSA-DTI), which effectively integrates the local information of drug molecules and target sequences with their respective global features. The neural fingerprint method is used in this model to extract global features of drug molecules, while the self-attention mechanism is utilized to enhance CNN’s capability in capturing the long-distance dependencies between the subsequences in the target amino acid sequence. In the feature fusion module, we improve the bilinear attention network by incorporating attention pooling, which enhances the model’s ability to learn local crucial interaction sites in the drug–target pair. The experimental results on three benchmark datasets demonstrated that NFSA-DTI outperformed all baseline models in predictive performance. Furthermore, case studies illustrated that our model could provide valuable insights for drug discovery. Moreover, our model offers molecular-level interpretations. Full article
(This article belongs to the Special Issue Molecular Computer Science and Artificial Intelligence for Drug Discovery)
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18 pages, 9608 KiB  
Article
Prediction of Drug-Target Affinity Using Attention Neural Network
by Xin Tang, Xiujuan Lei and Yuchen Zhang
Int. J. Mol. Sci. 2024, 25(10), 5126; https://doi.org/10.3390/ijms25105126 - 8 May 2024
Cited by 3 | Viewed by 1705
Abstract
Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it challenging to meet the current demands for swift and efficient drug [...] Read more.
Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it challenging to meet the current demands for swift and efficient drug development. Consequently, computational DTA prediction methods have emerged as indispensable tools for this research. In this article, we propose a novel deep learning algorithm named GRA-DTA, for DTA prediction. Specifically, we introduce Bidirectional Gated Recurrent Unit (BiGRU) combined with a soft attention mechanism to learn target representations. We employ Graph Sample and Aggregate (GraphSAGE) to learn drug representation, especially to distinguish the different features of drug and target representations and their dimensional contributions. We merge drug and target representations by an attention neural network (ANN) to learn drug-target pair representations, which are fed into fully connected layers to yield predictive DTA. The experimental results showed that GRA-DTA achieved mean squared error of 0.142 and 0.225 and concordance index reached 0.897 and 0.890 on the benchmark datasets KIBA and Davis, respectively, surpassing the most state-of-the-art DTA prediction algorithms. Full article
(This article belongs to the Special Issue Molecular Computer Science and Artificial Intelligence for Drug Discovery)
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20 pages, 21548 KiB  
Article
Generating Potential RET-Specific Inhibitors Using a Novel LSTM Encoder–Decoder Model
by Lu Liu, Xi Zhao and Xuri Huang
Int. J. Mol. Sci. 2024, 25(4), 2357; https://doi.org/10.3390/ijms25042357 - 17 Feb 2024
Viewed by 1258
Abstract
The receptor tyrosine kinase RET (rearranged during transfection) plays a vital role in various cell signaling pathways and is a critical factor in the development of the nervous system. Abnormal activation of the RET kinase can lead to several cancers, including thyroid cancer [...] Read more.
The receptor tyrosine kinase RET (rearranged during transfection) plays a vital role in various cell signaling pathways and is a critical factor in the development of the nervous system. Abnormal activation of the RET kinase can lead to several cancers, including thyroid cancer and non-small-cell lung cancer. However, most RET kinase inhibitors are multi-kinase inhibitors. Therefore, the development of an effective RET-specific inhibitor continues to present a significant challenge. To address this issue, we built a molecular generation model based on fragment-based drug design (FBDD) and a long short-term memory (LSTM) encoder–decoder structure to generate receptor-specific molecules with novel scaffolds. Remarkably, our model was trained with a molecular assembly accuracy of 98.4%. Leveraging the pre-trained model, we rapidly generated a RET-specific-candidate active-molecule library by transfer learning. Virtual screening based on our molecular generation model was performed, combined with molecular dynamics simulation and binding energy calculation, to discover specific RET inhibitors, and five novel molecules were selected. Further analyses indicated that two of these molecules have good binding affinities and synthesizability, exhibiting high selectivity. Overall, this investigation demonstrates the capacity of our model to generate novel receptor-specific molecules and provides a rapid method to discover potential drugs. Full article
(This article belongs to the Special Issue Molecular Computer Science and Artificial Intelligence for Drug Discovery)
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13 pages, 10852 KiB  
Article
SolPredictor: Predicting Solubility with Residual Gated Graph Neural Network
by Waqar Ahmad, Hilal Tayara, HyunJoo Shim and Kil To Chong
Int. J. Mol. Sci. 2024, 25(2), 715; https://doi.org/10.3390/ijms25020715 - 5 Jan 2024
Cited by 8 | Viewed by 2168
Abstract
Computational methods play a pivotal role in the pursuit of efficient drug discovery, enabling the rapid assessment of compound properties before costly and time-consuming laboratory experiments. With the advent of technology and large data availability, machine and deep learning methods have proven efficient [...] Read more.
Computational methods play a pivotal role in the pursuit of efficient drug discovery, enabling the rapid assessment of compound properties before costly and time-consuming laboratory experiments. With the advent of technology and large data availability, machine and deep learning methods have proven efficient in predicting molecular solubility. High-precision in silico solubility prediction has revolutionized drug development by enhancing formulation design, guiding lead optimization, and predicting pharmacokinetic parameters. These benefits result in considerable cost and time savings, resulting in a more efficient and shortened drug development process. The proposed SolPredictor is designed with the aim of developing a computational model for solubility prediction. The model is based on residual graph neural network convolution (RGNN). The RGNNs were designed to capture long-range dependencies in graph-structured data. Residual connections enable information to be utilized over various layers, allowing the model to capture and preserve essential features and patterns scattered throughout the network. The two largest datasets available to date are compiled, and the model uses a simplified molecular-input line-entry system (SMILES) representation. SolPredictor uses the ten-fold split cross-validation Pearson correlation coefficient R2 0.79±0.02 and root mean square error (RMSE) 1.03±0.04. The proposed model was evaluated using five independent datasets. Error analysis, hyperparameter optimization analysis, and model explainability were used to determine the molecular features that were most valuable for prediction. Full article
(This article belongs to the Special Issue Molecular Computer Science and Artificial Intelligence for Drug Discovery)
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17 pages, 2848 KiB  
Article
Literature-Based Discovery Predicts Antihistamines Are a Promising Repurposed Adjuvant Therapy for Parkinson’s Disease
by Gabriella Tandra, Amy Yoone, Rhea Mathew, Minzhi Wang, Chadwick M. Hales and Cassie S. Mitchell
Int. J. Mol. Sci. 2023, 24(15), 12339; https://doi.org/10.3390/ijms241512339 - 2 Aug 2023
Cited by 4 | Viewed by 4647
Abstract
Parkinson’s disease (PD) is a movement disorder caused by a dopamine deficit in the brain. Current therapies primarily focus on dopamine modulators or replacements, such as levodopa. Although dopamine replacement can help alleviate PD symptoms, therapies targeting the underlying neurodegenerative process are limited. [...] Read more.
Parkinson’s disease (PD) is a movement disorder caused by a dopamine deficit in the brain. Current therapies primarily focus on dopamine modulators or replacements, such as levodopa. Although dopamine replacement can help alleviate PD symptoms, therapies targeting the underlying neurodegenerative process are limited. The study objective was to use artificial intelligence to rank the most promising repurposed drug candidates for PD. Natural language processing (NLP) techniques were used to extract text relationships from 33+ million biomedical journal articles from PubMed and map relationships between genes, proteins, drugs, diseases, etc., into a knowledge graph. Cross-domain text mining, hub network analysis, and unsupervised learning rank aggregation were performed in SemNet 2.0 to predict the most relevant drug candidates to levodopa and PD using relevance-based HeteSim scores. The top predicted adjuvant PD therapies included ebastine, an antihistamine for perennial allergic rhinitis; levocetirizine, another antihistamine; vancomycin, a powerful antibiotic; captopril, an angiotensin-converting enzyme (ACE) inhibitor; and neramexane, an N-methyl-D-aspartate (NMDA) receptor agonist. Cross-domain text mining predicted that antihistamines exhibit the capacity to synergistically alleviate Parkinsonian symptoms when used with dopamine modulators like levodopa or levodopa–carbidopa. The relationship patterns among the identified adjuvant candidates suggest that the likely therapeutic mechanism(s) of action of antihistamines for combatting the multi-factorial PD pathology include counteracting oxidative stress, amending the balance of neurotransmitters, and decreasing the proliferation of inflammatory mediators. Finally, cross-domain text mining interestingly predicted a strong relationship between PD and liver disease. Full article
(This article belongs to the Special Issue Molecular Computer Science and Artificial Intelligence for Drug Discovery)
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