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Deep Learning in Molecular Science and Technology
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Deep Learning in Molecular Science and Technology

A special issue of Molecules (ISSN 1420-3049).

Deadline for manuscript submissions: 30 June 2025 | Viewed by 12477

Special Issue Editors

Prof. Dr. Zhenyang Lin

E-Mail Website
Guest Editor
Department of Chemistry, The Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong
Interests: computational chemistry; coordination chemistry; organometallic chemistry; reaction mechanism; structure and bonding
Prof. Dr. Haibin Su

E-Mail Website
Guest Editor
Department of Chemistry, The Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong
Interests: ab initio molecular dynamics; structure and bonding; computational materials science; physical chemistry; biophysical chemistry

Special Issue Information

Dear Colleagues,

We are witnessing a renaissance in molecular science and technology being driven by the application of deep learning technology to the increasingly available measured and computed data together with a rapidly growing body of literature. Breakthroughs in deep learning algorithms and hardware have greatly boosted the simulation and modelling of complex molecular systems at a level of accuracy necessary for quantitative analysis. This growing field offers unique opportunities in a wide spectrum of challenges. This Special Issue aims to present the current advances in methodology development and applications towards this “holy grail” of deep learning. We welcome the submission of both review and original research articles to this Special Issue.

Prof. Dr. Zhenyang Lin
Prof. Dr. Haibin Su
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.

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. Molecules is an international peer-reviewed open access semimonthly 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 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

  • deep learning
  • data informatics
  • physical chemistry
  • biochemistry
  • materials chemistry

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Published Papers (6 papers)

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Research

21 pages, 2153 KiB  
Article
Predicting the Toxicity of Drug Molecules with Selecting Effective Descriptors Using a Binary Ant Colony Optimization (BACO) Feature Selection Approach
by Yuanyuan Dan, Junhao Ruan, Zhenghua Zhu and Hualong Yu
Molecules 2025, 30(7), 1548; https://doi.org/10.3390/molecules30071548 - 31 Mar 2025
Viewed by 364
Abstract
Predicting the toxicity of drug molecules using in silico quantitative structure–activity relationship (QSAR) approaches is very helpful for guiding safe drug development and accelerating the drug development procedure. The ongoing development of machine learning techniques has made this task easier and more accurate, [...] Read more.
Predicting the toxicity of drug molecules using in silico quantitative structure–activity relationship (QSAR) approaches is very helpful for guiding safe drug development and accelerating the drug development procedure. The ongoing development of machine learning techniques has made this task easier and more accurate, but it still suffers negative effects from both the severely skewed distribution of active/inactive chemicals and relatively high-dimensional feature distribution. To simultaneously address both of these issues, a binary ant colony optimization feature selection algorithm, called BACO, is proposed in this study. Specifically, it divides the labeled drug molecules into a training set and a validation set multiple times; with each division, the ant colony seeks an optimal feature group that aims to maximize the weighted combination of three specific class imbalance performance metrics (F-measure, G-mean, and MCC) on the validation set. Then, after running all divisions, the frequency of each feature (descriptor) that emerges in the optimal feature groups is calculated and ranked in descending order. Only those high-frequency features are used to train a support vector machine (SVM) and construct the structure–activity relationship (SAR) prediction model. The experimental results for the 12 datasets in the Tox21 challenge, represented by the Modred descriptor calculator, show that the proposed BACO method significantly outperforms several traditional feature selection approaches that have been widely used in QSAR analysis. It only requires a few to a few dozen descriptors for most datasets to exhibit its best performance, which shows its effectiveness and potential application value in cheminformatics. Full article
(This article belongs to the Special Issue Deep Learning in Molecular Science and Technology)
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15 pages, 2590 KiB  
Article
A Machine Learning Model for Predicting the Propagation Rate Coefficient in Free-Radical Polymerization
by Yiming Wang, Yue Fang, Haifan Zhou and Hanyu Gao
Molecules 2024, 29(19), 4694; https://doi.org/10.3390/molecules29194694 - 3 Oct 2024
Viewed by 1460
Abstract
The propagation rate coefficient (kp) is one of the most crucial kinetic parameters in free-radical polymerization (FRP) as it directly governs the rate of polymerization and the resulting molecular weight distribution. The kp in FRP can typically be obtained [...] Read more.
The propagation rate coefficient (kp) is one of the most crucial kinetic parameters in free-radical polymerization (FRP) as it directly governs the rate of polymerization and the resulting molecular weight distribution. The kp in FRP can typically be obtained through experimental measurements or quantum chemical calculations, both of which can be time consuming and resource intensive. Herein, we developed a machine learning model based solely on the structural features of monomers involved in FRP, utilizing molecular embedding and a Lasso regression algorithm to predict kp more efficiently and accurately. The result shows that the model achieves a mean absolute percentage error (MAPE) of only 5.49% in the predictions for four new monomers, which indicates that the model exhibits strong generalization capabilities and provides reliable and robust predictions. In addition, this model can accurately predict the influence of the ester side chain length of (meth)acrylates on kp, aligning well with established scientific knowledge. This approach offers a straightforward and practical model for other researchers to rapidly obtain accurate kp values by employing monomer structural information. The model is sufficiently general to apply to a wide range of (meth)acrylate and butadiene FRP monomers, thereby supporting kinetic modeling of polymerization reactions. Full article
(This article belongs to the Special Issue Deep Learning in Molecular Science and Technology)
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Graphical abstract

29 pages, 2253 KiB  
Article
Clustering Molecules at a Large Scale: Integrating Spectral Geometry with Deep Learning
by Ömer Akgüller, Mehmet Ali Balcı and Gabriela Cioca
Molecules 2024, 29(16), 3902; https://doi.org/10.3390/molecules29163902 - 17 Aug 2024
Cited by 1 | Viewed by 1818
Abstract
This study conducts an in-depth analysis of clustering small molecules using spectral geometry and deep learning techniques. We applied a spectral geometric approach to convert molecular structures into triangulated meshes and used the Laplace–Beltrami operator to derive significant geometric features. By examining the [...] Read more.
This study conducts an in-depth analysis of clustering small molecules using spectral geometry and deep learning techniques. We applied a spectral geometric approach to convert molecular structures into triangulated meshes and used the Laplace–Beltrami operator to derive significant geometric features. By examining the eigenvectors of these operators, we captured the intrinsic geometric properties of the molecules, aiding their classification and clustering. The research utilized four deep learning methods: Deep Belief Network, Convolutional Autoencoder, Variational Autoencoder, and Adversarial Autoencoder, each paired with k-means clustering at different cluster sizes. Clustering quality was evaluated using the Calinski–Harabasz and Davies–Bouldin indices, Silhouette Score, and standard deviation. Nonparametric tests were used to assess the impact of topological descriptors on clustering outcomes. Our results show that the DBN + k-means combination is the most effective, particularly at lower cluster counts, demonstrating significant sensitivity to structural variations. This study highlights the potential of integrating spectral geometry with deep learning for precise and efficient molecular clustering. Full article
(This article belongs to the Special Issue Deep Learning in Molecular Science and Technology)
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13 pages, 820 KiB  
Communication
Catalytic Activity of 2-Imino-1,10-phenthrolyl Fe/Co Complexes via Linear Machine Learning
by Zubair Sadiq, Wenhong Yang, Md Mostakim Meraz, Weisheng Yang and Wen-Hua Sun
Molecules 2024, 29(10), 2313; https://doi.org/10.3390/molecules29102313 - 15 May 2024
Cited by 1 | Viewed by 1071
Abstract
In anticipation of the correlations between catalyst structures and their properties, the catalytic activities of 2-imino-1,10-phenanthrolyl iron and cobalt metal complexes are quantitatively investigated via linear machine learning (ML) algorithms. Comparatively, the Ridge Regression (RR) model has captured more robust predictive performance compared [...] Read more.
In anticipation of the correlations between catalyst structures and their properties, the catalytic activities of 2-imino-1,10-phenanthrolyl iron and cobalt metal complexes are quantitatively investigated via linear machine learning (ML) algorithms. Comparatively, the Ridge Regression (RR) model has captured more robust predictive performance compared with other linear algorithms, with a correlation coefficient value of R2 = 0.952 and a cross-validation value of Q2 = 0.871. It shows that different algorithms select distinct types of descriptors, depending on the importance of descriptors. Through the interpretation of the RR model, the catalytic activity is potentially related to the steric effect of substituents and negative charged groups. This study refines descriptor selection for accurate modeling, providing insights into the variation principle of catalytic activity. Full article
(This article belongs to the Special Issue Deep Learning in Molecular Science and Technology)
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Graphical abstract

14 pages, 2680 KiB  
Article
Incorporating Domain Knowledge and Structure-Based Descriptors for Machine Learning: A Case Study of Pd-Catalyzed Sonogashira Reactions
by Kalok Chan, Long Thanh Ta, Yong Huang, Haibin Su and Zhenyang Lin
Molecules 2023, 28(12), 4730; https://doi.org/10.3390/molecules28124730 - 13 Jun 2023
Viewed by 3853
Abstract
Machine learning has revolutionized information processing for large datasets across various fields. However, its limited interpretability poses a significant challenge when applied to chemistry. In this study, we developed a set of simple molecular representations to capture the structural information of ligands in [...] Read more.
Machine learning has revolutionized information processing for large datasets across various fields. However, its limited interpretability poses a significant challenge when applied to chemistry. In this study, we developed a set of simple molecular representations to capture the structural information of ligands in palladium-catalyzed Sonogashira coupling reactions of aryl bromides. Drawing inspiration from human understanding of catalytic cycles, we used a graph neural network to extract structural details of the phosphine ligand, a major contributor to the overall activation energy. We combined these simple molecular representations with an electronic descriptor of aryl bromide as inputs for a fully connected neural network unit. The results allowed us to predict rate constants and gain mechanistic insights into the rate-limiting oxidative addition process using a relatively small dataset. This study highlights the importance of incorporating domain knowledge in machine learning and presents an alternative approach to data analysis. Full article
(This article belongs to the Special Issue Deep Learning in Molecular Science and Technology)
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16 pages, 818 KiB  
Article
mech2d: An Efficient Tool for High-Throughput Calculation of Mechanical Properties for Two-Dimensional Materials
by Haidi Wang, Tao Li, Xiaofeng Liu, Weiduo Zhu, Zhao Chen, Zhongjun Li and Jinlong Yang
Molecules 2023, 28(11), 4337; https://doi.org/10.3390/molecules28114337 - 25 May 2023
Cited by 8 | Viewed by 2865
Abstract
Two-dimensional (2D) materials have been a research hot topic in the passed decades due to their unique and fascinating properties. Among them, mechanical properties play an important role in their application. However, there lacks an effective tool for high-throughput calculating, analyzing and visualizing [...] Read more.
Two-dimensional (2D) materials have been a research hot topic in the passed decades due to their unique and fascinating properties. Among them, mechanical properties play an important role in their application. However, there lacks an effective tool for high-throughput calculating, analyzing and visualizing the mechanical properties of 2D materials. In this work, we present the mech2d package, a highly automated toolkit for calculating and analyzing the second-order elastic constants (SOECs) tensor and relevant properties of 2D materials by considering their symmetry. In the mech2d, the SOECs can be fitted by both the strain–energy and stress–strain approaches, where the energy or strain can be calculated by a first-principles engine, such as VASP. As a key feature, the mech2d package can automatically submit and collect the tasks from a local or remote machine with robust fault-tolerant ability, making it suitable for high-throughput calculation. The present code has been validated by several common 2D materials, including graphene, black phosphorene, GeSe2 and so on. Full article
(This article belongs to the Special Issue Deep Learning in Molecular Science and Technology)
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