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Introduction: Understanding the interactions between functional groups, ligands, molecular fragments, as well as whole molecules, is critical in modern drug discovery. Key to this endeavor is the theoretical development of the fundamental inter-particle forces in play and their implementation in numerous software packages that allow the calculation of interaction energies at varying levels of theory ranging from the entirely classical at one extreme to the fully quantum mechanical at the other.
Areas Covered: In this review, the authors consider the concept of an intermolecular potential energy function and its separation into short- and long-range regions. This is followed by a summary of the perturbation theory calculation of the electrostatic, induction and dispersion energy shifts by expanding the charge distribution in terms of source multipole moments. Next, the authors outline the construction of a typical molecular force field and its parameterization; they also discuss the fundamental background of molecular dynamics (MD) simulations, their implementation in several well-known software packages and their deployment in modern computational drug discovery, including recent work with Artificial Intelligence and Machine Learning techniques. Papers cited by SSC were the result of a literature search conducted using PubMed and Google Scholar during Jan-July 2025 as well as from the authors' personal literature stock.
Expert Opinion: While the underlying quantum mechanical theory of intermolecular forces is well-known, their accurate and reliable calculation for an ever-growing variety of increasingly complex systems mirrors the advances in computational hardware on which such simulations are performed. Coupled with emerging machine learning techniques, this allows for the rapid and efficient computer-aided discovery of potential new drug candidates, in the process revolutionizing research and development in both academia and industry.
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http://dx.doi.org/10.1080/17460441.2025.2555271 | DOI Listing |
Mol Divers
September 2025
Department of Biotechnology, National Institute of Technology Raipur, Raipur, Chhattisgarh, 492001, India.
Traditional drug discovery methods like high-throughput screening and molecular docking are slow and costly. This study introduces a machine learning framework to predict bioactivity (pIC₅₀) and identify key molecular properties and structural features for targeting Trypanothione reductase (TR), Protein kinase C theta (PKC-θ), and Cannabinoid receptor 1 (CB1) using data from the ChEMBL database. Molecular fingerprints, generated via PaDEL-Descriptor and RDKit, encoded structural features as binary vectors.
View Article and Find Full Text PDFMol Divers
September 2025
State Key Laboratory Basis of Xinjiang Indigenous Medicinal Plants Resource Utilization, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, Xinjiang, China.
Aurora kinases are a group of serine/threonine kinases essential for cell mitosis, comprising Aurora A, B, and C. However, the Aurora B is overexpressed in multiple tumors and the aurone has been proved to exhibit potent inhibitory activity against Aurora B kinase by our group. The indolinone was considered as an aurone scaffold hopping analog, and the indolinone-based Aurora B inhibitor library (3577 molecules) was constructed by FBDD strategy.
View Article and Find Full Text PDFMol Divers
September 2025
Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, 211198, China.
Drug absorption significantly influences pharmacokinetics. Accurately predicting human oral bioavailability (HOB) is essential for optimizing drug candidates and improving clinical success rates. The traditional method based on experiment is a common way to obtain HOB, but the experimental method is time-consuming and costly.
View Article and Find Full Text PDFMol Biol Rep
September 2025
School of Pharmacy, Heilongjiang University of Chinese Medicine, NO 24 Heping Road, 150040, Harbin, P. R. China.
Lysosome-dependent cell death (LDCD) is a regulated form of cell death initiated by increased lysosomal membrane permeability, leading to the cytoplasmic release of lysosomal enzymes and subsequent cellular damage. Molecular mechanisms controlling LDCD include lysosomal membrane instability and lysosomal enzyme release, which together lead to cell damage. A more profound comprehension of these underlying mechanisms may reveal new therapeutic targets for diseases associated with lysosomal dysfunction.
View Article and Find Full Text PDFNanoscale
September 2025
Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, Higher Educational Key Laboratory for Nano Biomedical Technology of Fujian Province, The School of Pharmacy, Fujian Medical University, Fuzhou, Fujian 350122, People's Republic of China.
The rational design of non-precious metal catalysts as a replacement for Pd is of great importance for catalyzing various important chemical reactions. To realize this purpose, the palladium-like superatom NbN was doped into a defective graphene quantum dot (GQD) model with a double-vacancy site to design a novel single superatom catalyst, namely, NbN@GQD, based on density functional theory (DFT), and its catalytic activity for the Suzuki reaction was theoretically investigated. Our results reveal that this designed catalyst exhibits satisfactory activity with a small rate-limiting energy barrier of 25.
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