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Recent Advances in Artificial Intelligence-Based Drug Discovery
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Recent Advances in Artificial Intelligence-Based Drug Discovery

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 16548

Special Issue Editors

Prof. Dr. Xing Chen

E-Mail Website
Guest Editor
Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China
Interests: bioinformatics; computational biology; systems biology; non-coding RNA; drug discovery; machine learning; network algorithm
Special Issues, Collections and Topics in MDPI journals
Dr. Qi Zhao

E-Mail Website1 Website2
Guest Editor
School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, China
Interests: bioinformatics; data science; computational biology; machine learning; artificial intelligence; ncRNA; prognostic markers

Special Issue Information

Dear Colleagues,

Drug discovery is the process through which potential new compounds are identified by means of biology, chemistry, and pharmacology. Billions of dollars are invested annually in research aimed at discovering, designing, and developing new drugs for a wide range of diseases. However, the research and development of novel drugs are still time-consuming and sometimes difficult to accomplish. With the development of new experimental techniques, vast amounts of datasets now flow through the different stages of drug development. Biomedical research, especially for the field of drug discovery, is currently experiencing a global paradigm shift with artificial intelligence (AI) technologies and their application to “Big Data”. Therefore, a key challenge for future drug discovery research is the development of powerful AI-based computational tools that can capture multiple dimensions of biomedical insights. We invite investigators to contribute research articles and reviews describing recent findings which use AI-based computational techniques for research in computer-aided drug discovery.

Prof. Dr. Xing Chen
Prof. Dr. Qi Zhao
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 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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

  • drug–target interactions prediction
  • small molecule drug–noncoding RNA association prediction
  • drug–pathway interaction prediction
  • synergistic drug combination prediction
  • anti-cancer drug response prediction
  • drug–drug interaction prediction
  • computer-aided drug design
  • computational drug repositioning

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Published Papers (4 papers)

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Research

9 pages, 591 KiB  
Article
Improving Chemical Reaction Prediction with Unlabeled Data
by Yu Xie, Yuyang Zhang, Ka-Chun Wong, Meixia Shi and Chengbin Peng
Molecules 2022, 27(18), 5967; https://doi.org/10.3390/molecules27185967 - 14 Sep 2022
Viewed by 2049
Abstract
Predicting products of organic chemical reactions is useful in chemical sciences, especially when one or more reactants are new organics. However, the performance of traditional learning models heavily relies on high-quality labeled data. In this work, to utilize unlabeled data for better prediction [...] Read more.
Predicting products of organic chemical reactions is useful in chemical sciences, especially when one or more reactants are new organics. However, the performance of traditional learning models heavily relies on high-quality labeled data. In this work, to utilize unlabeled data for better prediction performance, we propose a method that combines semi-supervised learning with graph convolutional neural networks for chemical reaction prediction. First, we propose a Mean Teacher Weisfeiler–Lehman Network to find the reaction centers. Then, we construct the candidate product set. Finally, we use an Improved Weisfeiler–Lehman Difference Network to rank candidate products. Experimental results demonstrate that, with 400k labeled data, our framework can improve the top-5 accuracy by 0.7% using 35k unlabeled data. When the proportion of unlabeled data increases, the performance gain can be larger. For example, with 80k labeled data and 35k unlabeled data, the performance gain with our framework can be 1.8%. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence-Based Drug Discovery)
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15 pages, 1646 KiB  
Article
Prediction of Drug–Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding
by Jiaxin Li, Xixin Yang, Yuanlin Guan and Zhenkuan Pan
Molecules 2022, 27(16), 5131; https://doi.org/10.3390/molecules27165131 - 12 Aug 2022
Cited by 8 | Viewed by 2111
Abstract
Nowadays, drug–target interactions (DTIs) prediction is a fundamental part of drug repositioning. However, on the one hand, drug–target interactions prediction models usually consider drugs or targets information, which ignore prior knowledge between drugs and targets. On the other hand, models incorporating priori knowledge [...] Read more.
Nowadays, drug–target interactions (DTIs) prediction is a fundamental part of drug repositioning. However, on the one hand, drug–target interactions prediction models usually consider drugs or targets information, which ignore prior knowledge between drugs and targets. On the other hand, models incorporating priori knowledge cannot make interactions prediction for under-studied drugs and targets. Hence, this article proposes a novel dual-network integrated logistic matrix factorization DTIs prediction scheme (Ro-DNILMF) via a knowledge graph embedding approach. This model adds prior knowledge as input data into the prediction model and inherits the advantages of the DNILMF model, which can predict under-studied drug–target interactions. Firstly, a knowledge graph embedding model based on relational rotation (RotatE) is trained to construct the interaction adjacency matrix and integrate prior knowledge. Secondly, a dual-network integrated logistic matrix factorization prediction model (DNILMF) is used to predict new drugs and targets. Finally, several experiments conducted on the public datasets are used to demonstrate that the proposed method outperforms the single base-line model and some mainstream methods on efficiency. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence-Based Drug Discovery)
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18 pages, 4438 KiB  
Article
Accelerating AutoDock Vina with GPUs
by Shidi Tang, Ruiqi Chen, Mengru Lin, Qingde Lin, Yanxiang Zhu, Ji Ding, Haifeng Hu, Ming Ling and Jiansheng Wu
Molecules 2022, 27(9), 3041; https://doi.org/10.3390/molecules27093041 - 9 May 2022
Cited by 45 | Viewed by 7422
Abstract
AutoDock Vina is one of the most popular molecular docking tools. In the latest benchmark CASF-2016 for comparative assessment of scoring functions, AutoDock Vina won the best docking power among all the docking tools. Modern drug discovery is facing a common scenario of [...] Read more.
AutoDock Vina is one of the most popular molecular docking tools. In the latest benchmark CASF-2016 for comparative assessment of scoring functions, AutoDock Vina won the best docking power among all the docking tools. Modern drug discovery is facing a common scenario of large virtual screening of drug hits from huge compound databases. Due to the seriality characteristic of the AutoDock Vina algorithm, there is no successful report on its parallel acceleration with GPUs. Current acceleration of AutoDock Vina typically relies on the stack of computing power as well as the allocation of resource and tasks, such as the VirtualFlow platform. The vast resource expenditure and the high access threshold of users will greatly limit the popularity of AutoDock Vina and the flexibility of its usage in modern drug discovery. In this work, we proposed a new method, Vina-GPU, for accelerating AutoDock Vina with GPUs, which is greatly needed for reducing the investment for large virtual screens and also for wider application in large-scale virtual screening on personal computers, station servers or cloud computing, etc. Our proposed method is based on a modified Monte Carlo using simulating annealing AI algorithm. It greatly raises the number of initial random conformations and reduces the search depth of each thread. Moreover, a classic optimizer named BFGS is adopted to optimize the ligand conformations during the docking progress, before a heterogeneous OpenCL implementation was developed to realize its parallel acceleration leveraging thousands of GPU cores. Large benchmark tests show that Vina-GPU reaches an average of 21-fold and a maximum of 50-fold docking acceleration against the original AutoDock Vina while ensuring their comparable docking accuracy, indicating its potential for pushing the popularization of AutoDock Vina in large virtual screens. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence-Based Drug Discovery)
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Graphical abstract

16 pages, 2011 KiB  
Article
Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs
by Yue-Hua Feng and Shao-Wu Zhang
Molecules 2022, 27(9), 3004; https://doi.org/10.3390/molecules27093004 - 7 May 2022
Cited by 19 | Viewed by 4194
Abstract
The treatment of complex diseases by using multiple drugs has become popular. However, drug-drug interactions (DDI) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. Therefore, for polypharmacy safety it is crucial to identify DDIs and explore their [...] Read more.
The treatment of complex diseases by using multiple drugs has become popular. However, drug-drug interactions (DDI) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. Therefore, for polypharmacy safety it is crucial to identify DDIs and explore their underlying mechanisms. The detection of DDI in the wet lab is expensive and time-consuming, due to the need for experimental research over a large volume of drug combinations. Although many computational methods have been developed to predict DDIs, most of these are incapable of predicting potential DDIs between drugs within the DDI network and new drugs from outside the DDI network. In addition, they are not designed to explore the underlying mechanisms of DDIs and lack interpretative capacity. Thus, here we propose a novel method of GNN-DDI to predict potential DDIs by constructing a five-layer graph attention network to identify k-hops low-dimensional feature representations for each drug from its chemical molecular graph, concatenating all identified features of each drug pair, and inputting them into a MLP predictor to obtain the final DDI prediction score. The experimental results demonstrate that our GNN-DDI is suitable for each of two DDI predicting scenarios, namely the potential DDIs among known drugs in the DDI network and those between drugs within the DDI network and new drugs from outside DDI network. The case study indicates that our method can explore the specific drug substructures that lead to the potential DDIs, which helps to improve interpretability and discover the underlying interaction mechanisms of drug pairs. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence-Based Drug Discovery)
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