Computational Biology and Chemistry最新文献

{"title":"Identification of Therapeutic Compounds Targeting Phosphatidylinositol 3-Kinase (PI3K) Through Molecular Docking, Dynamics Simulation, and DFT Calculations","authors":"Jehad Zuhair Tayyeb ,&nbsp;Imren Bayıl ,&nbsp;Taha Alqahtani ,&nbsp;Gabriel Vinícius Rolim Silva ,&nbsp;Guilherme Bastos Alves ,&nbsp;Al-Anood M. Al-Dies ,&nbsp;Abdelkrim Guendouzi ,&nbsp;Jonas Ivan Nobre Oliveira ,&nbsp;Magdi E.A. Zaki","doi":"10.1016/j.compbiolchem.2025.108433","DOIUrl":"10.1016/j.compbiolchem.2025.108433","url":null,"abstract":"<div><div>Cancer is one of the leading causes of death worldwide and characterized by uncontrolled cell proliferation. The phosphatidylinositol 3-kinase (PI3K) is an enzyme, which is essential for regulating cell growth and survival, is often dysregulated in tumors. Currently available PI3K inhibitors (like Duvelisib) have significant side effects, highlighting the need for safer therapeutics. Gallic acid, a natural phenolic compound with remarkable antineoplastic properties, showcases a promising scaffold for drug development. The aim of this study is to identify potential PI3K inhibitors from gallic acid derivatives using advanced computational techniques such as PASS prediction, molecular docking, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis, density functional theory (DFT) calculations, and molecular dynamics (MD) simulations. Five derivatives 21, 37, 44, 68 and 75 were selected based on their predicted antineoplastic activity among 90 derivatives, as well as the control drug Duvelisib. Compound 68 proved to be the most promising candidate, exhibiting strong binding affinity to the PI3K receptor, forming multiple hydrogen bonds with key residues, and showing stable interactions over 500 ns MD simulation. ADMET analysis revealed that compound 68 had favorable pharmacokinetic properties. Compound 21 also showed strong binding affinity but exhibited limitations in its pharmacokinetic profile. This study aims to improve our understanding of ligand-protein dynamics in PI3K inhibition and highlight the potential of gallic acid derivatives in developing safer and more effective PI3K inhibitors for cancer therapy. Our results support further experimental validation of compound 68 and suggest that gallic acid derivatives could contribute to the development of safer therapies.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108433"},"PeriodicalIF":2.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drug–drug interaction prediction based on graph contrastive learning and dual-view fusion","authors":"Shanyang Ding,&nbsp;Dongjiang Niu,&nbsp;Mingxuan Li,&nbsp;Zhixin Zhang,&nbsp;Zhen Li","doi":"10.1016/j.compbiolchem.2025.108426","DOIUrl":"10.1016/j.compbiolchem.2025.108426","url":null,"abstract":"<div><div>Drug–drug interaction (DDI) is important in drug research and are one of the major causes of morbidity and mortality. The deep learning methods can automatically extract drug features from molecular graphs or drug-related networks, which improves the performance of DDI prediction. However, there is noise and incomplete data in existing datasets, and the volume of dataset is limited. In order to fully utilize the knowledge graph network and the molecular structure, we propose a dual-view fusion model GDF-DDI. In one view, the knowledge graph network and drug similarity network are constructed as the global information, and two graph convolution operations are implemented on both networks to extract drug embeddings. Subsequently, layer wise graph contrastive learning is performed to update the drug embeddings to captures richer semantic information. In the other view, the self-supervised learning is utilized to extract more comprehensive embedding of drugs. The embeddings under two views are concatenated to cover the global and local DDI information. The comparative experiments on two datasets show that our model outperforms other recent and state-of-the-art baselines.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108426"},"PeriodicalIF":2.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporating time as a third dimension in transcriptomic analysis using machine learning and explainable AI","authors":"Zubaida Said Ameen ,&nbsp;Auwalu Saleh Mubarak ,&nbsp;Mohamed Hamad ,&nbsp;Rifat Hamoudi ,&nbsp;Sherlyn Jemimah ,&nbsp;Dilber Uzun Ozsahin ,&nbsp;Mawieh Hamad","doi":"10.1016/j.compbiolchem.2025.108432","DOIUrl":"10.1016/j.compbiolchem.2025.108432","url":null,"abstract":"<div><div>Transcriptomic data analysis entails the measurement of RNA transcript (gene expression products) abundance in a cell or a cell population at a single point in time. In other words, transcriptomics as it is currently practiced is two-dimensional (2DTA). Gene expression profiling by 2DTA has proven invaluable in furthering our understanding of numerous biological processes in health and disease. That said, shortcomings including technical variability, small sample size, differential rates of transcript decay, and the lack of linearity between transcript abundance and functionality or the formation of functional proteins limit the interpretive utility and generalizability of transcriptomic data. 2DTA utility may also be constrained by its reliance on RNA extracts obtained at a single time point. In other words, much like judging a movie by a single frame, 2DTA can only provide a snapshot of the transcriptome at time of RNA extraction. Whether this perceived “temporality” problem is real and whether it has any bearing on transcriptomic data interpretation have yet to be addressed. To investigate this problem, 25 publicly available datasets relating to MCF-7 cells, where RNA extracts obtained at 12- or 48-hours post-culture were subjected to transcriptomic analysis. The individual datasets were downloaded and compiled into two separate datasets (MCF-7 U12hr and MCF-7 U48hr). To comparatively analyze the two compiled datasets, three machine learning approaches (decision trees (DT), random forests (RF), and XGBoost (Extreme Gradient Boosting)) were used as classifiers to search for genes with distinct expression patterns between the two groups. Shapley additive explanation (SHAP), an explainable AI method, was used to assess the fundamental principles of the DT, RF, and XGBoost models. Coefficient of Determination (DC), Mean Absolute Error (MAE), and Mean Squared Error (MSE) were used to evaluate the models. The results show that the two datasets exhibited very significant gene expression patterns. The XGBoost model performed better than the DT or RF models with MSE, MAE, and DC values of 0.00028, 0.00028, and 0.95778 respectively. These observations suggest that time, as a third dimension, can impact transcriptomic data interpretation and that machine learning and explainable AI are useful tools in resolving the temporality problem in transcriptomics.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108432"},"PeriodicalIF":2.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of potential therapeutic targets for stroke using data mining, network analysis, enrichment, and docking analysis","authors":"Mahdi Hatamipour ,&nbsp;Hossein Saremi ,&nbsp;Prashant Kesharwani ,&nbsp;Amirhossein Sahebkar","doi":"10.1016/j.compbiolchem.2025.108431","DOIUrl":"10.1016/j.compbiolchem.2025.108431","url":null,"abstract":"<div><div>Stroke is a leading cause of disability and death worldwide. In this study, we identified potential therapeutic targets for stroke using a data mining, network analysis, enrichment, and docking analysis approach. We first identified 1991 genes associated with stroke from two publicly available databases: GeneCards and DisGeNET. We then constructed a protein-protein interaction (PPI) network using the STRING database and identified 1301 nodes and 5413 edges. We used Metascape to perform GO enrichment analysis and KEGG pathway enrichment analysis. The results of these analyses identified ten hub genes (TNF, IL6, ACTB, AKT1, IL1B, TP53, VEGFA, STAT3, CASP3, and CTNNB1) and five KEGG pathways (cancer, lipid and atherosclerosis, cytokine–cytokine receptor interaction, AGE RAGE signaling pathway in complications, and TNF signaling pathway) that are enriched in stroke genes. We then performed molecular docking analysis to screen potential drug candidates for these targets. The results of this analysis identified several promising drug candidates that could be used to develop new therapeutic strategies for stroke.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108431"},"PeriodicalIF":2.6,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovery of natural MCL1 inhibitors using pharmacophore modelling, QSAR, docking, ADMET, molecular dynamics, and DFT analysis","authors":"Uddalak Das ,&nbsp;Tathagata Chanda ,&nbsp;Jitendra Kumar ,&nbsp;Anitha Peter","doi":"10.1016/j.compbiolchem.2025.108427","DOIUrl":"10.1016/j.compbiolchem.2025.108427","url":null,"abstract":"<div><div>Mcl-1, a member of the Bcl-2 family, is a crucial regulator of apoptosis, frequently overexpressed in various cancers, including lung, breast, pancreatic, cervical, ovarian cancers, leukemia, and lymphoma. Its anti-apoptotic function allows tumor cells to evade cell death and contributes to drug resistance, making it an essential target for anticancer drug development. This study aimed to discover potent antileukemic compounds targeting Mcl-1. We selected diverse molecules from the BindingDB database to construct a structure-based pharmacophore model, which facilitated the virtual screening of 407,270 compounds from the COCONUT database. An e-pharmacophore model was developed using the co-crystallized inhibitor, followed by QSAR modeling to estimate IC₅₀ values and filter compounds with predicted values below the median. The top hits underwent molecular docking and MMGBSA binding energy calculations against Mcl-1, resulting in the selection of two promising candidates for further ADMET analysis. DFT calculations assessed their electronic properties, confirming favorable reactivity profiles of the screened compounds. Predictions for physicochemical and ADMET properties aligned with expected bioactivity and safety. Molecular dynamics simulations further validated their strong binding affinity and stability, positioning them as potential Mcl-1 inhibitors. Our comprehensive computational approach highlights these compounds as promising antileukemic agents, with future <em>in vivo</em> and <em>in vitro</em> validation recommended for further confirmation.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108427"},"PeriodicalIF":2.6,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A computational journey in anticancer drug discovery: Exploring AKT1 inhibition by novel oxadiazoles using molecular docking, ADMET, density functional theory and molecular dynamic simulation","authors":"Gauri Alias Pooja Naik ,&nbsp;Omkar Paradkar ,&nbsp;Vishnu Sharma ,&nbsp;Shubham Kumar ,&nbsp;Pawan Gupta ,&nbsp;Pankaj Wadhwa","doi":"10.1016/j.compbiolchem.2025.108425","DOIUrl":"10.1016/j.compbiolchem.2025.108425","url":null,"abstract":"<div><div>AKT, also called (PKB) Protein Kinase B, is critical for cell proliferation, metabolism, and survival, with its dysfunction linked to various diseases, including cancer. The oxadiazole nucleus has demonstrated significant anticancer activity in literature surveys. The motivation for conducting this study stems from the fact that, despite numerous investigations into novel therapeutic targets and lead compounds, the inhibition of AKT1 presents limited treatment options due to various adverse drug reactions and specificity issues, given that AKT1 exists in three isoforms. So, this study aimed to identify top-hit molecules with 1,3,4 oxadiazole scaffold targeting the AKT1 enzyme by ligand-based virtual screening to produce a dataset library from PubChem database, structure-based virtual screening followed by ADMET profiling, DFT, and molecular dynamic simulation study as an innovative approach, as there is a dearth of AKT1 inhibitors that comprise oxadiazole in the market and clinical trials. The study employs a combination of advanced computational methods, including virtual screening, molecular docking, molecular dynamics simulations, density functional theory calculations, and ADMET predictions. This comprehensive approach offers a thorough investigation of prospective drug candidates. A comprehensive pharmacoinformatic analysis was conducted on a library of compounds containing oxadiazole rings. A total of 1000 compounds were analyzed through virtual screening utilizing molecular docking and subsequent validation, aiming to identify inhibitors that exhibit a strong affinity for binding for AKT1 (PDB ID: 3O96). Thus, 24 compounds demonstrating binding affinities analogous to the standard emerged as the most promising medicinal prospects and underwent ADMET profiling, and DFT studies followed by a molecular dynamic study on the best 2 compounds. Moreover, these compounds found by ADMET profiling showed suitable pharmacokinetic and pharmacodynamic profiles and were non-toxic using DFT analysis determining ideal structural characteristics. Especially showing comparable stability to the reference molecule over 200 ns in MD simulations, the best top 2 hit compounds having binding affinity −10.7 kcal/mol for <strong>PCOS_ 133 (CID-164189)</strong> and −11.6 kcal/mol for <strong>PCOS3_42 (CID-158973)</strong> emerged as potential AKT1 inhibitors for cancer therapy in comparison to −11.6 kcal/mol and −14.7 kcal/mol binding affinity of Miransertib reference drug and IQO cocrystallized ligand of AKT1 protein PDB code 3O96. LEU-210, LEU-264, ASP-292, and TRP-80 are the important amino acid residues required for AKT1 inhibition. These results provide important new perspectives for the rational design and optimization of oxadiazole-based AKT1/PKB inhibitors, therefore laying a strong basis for experimental validation including further in-vitro and in vivo studies and PKB inhibitor development.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108425"},"PeriodicalIF":2.6,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A critical address to advancements and challenges in computational strategies for structural prediction of protein in recent past","authors":"Nida Fatima Ali ,&nbsp;Shumaila Khan ,&nbsp;Saadia Zahid","doi":"10.1016/j.compbiolchem.2025.108430","DOIUrl":"10.1016/j.compbiolchem.2025.108430","url":null,"abstract":"<div><div>Protein structure prediction has undergone significant advancements, driven by the limitations of experimental techniques like X-ray crystallography, NMR, and cryo-EM, which are costly and time-consuming. To bridge the gap between protein sequences and their structures, computational methods have emerged as essential tools. Traditional approaches such as homology modeling, threading, and <em>ab initio</em> folding made progress but often lacked atomic-level precision. The field has been revolutionized by deep learning-based models such as AlphaFold2, RoseTTAFold, and OpenFold, which have demonstrated unprecedented accuracy in predicting protein structures. These AI-driven models leverage vast datasets and neural networks to generate highly reliable structural predictions, sometimes rivaling experimental methods. This review explores the historical evolution of computational protein structure prediction, analyzing the strengths and weaknesses of state-of-the-art models. These models have broad applications in fields such as drug discovery, enzyme engineering, and disease-related protein modeling. However, challenges remain, including the need for extensive training data, computational resource requirements, and difficulties in modeling protein dynamics, intrinsically disordered regions, and protein-protein interactions. Future directions in the field include improving AI models to address current limitations, better integration with experimental techniques, and extending predictions to protein complexes and post-translational modifications. By continuing to refine these methods, computational protein structure prediction will further enhance biomedical research and therapeutic design, reshaping the landscape of structural biology and computational biophysics.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108430"},"PeriodicalIF":2.6,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing novel analogues of nilutamide as a human androgen receptor antagonist: A detailed investigation of drug design using a bioisosteric methodology including ADMET profiling, molecular docking studies and molecular dynamics simulation","authors":"Ajay Kumar Gupta ,&nbsp;Yogita Sahu ,&nbsp;Dipti Pal ,&nbsp;Neeraj Kumar ,&nbsp;Sanmati Kumar Jain","doi":"10.1016/j.compbiolchem.2025.108424","DOIUrl":"10.1016/j.compbiolchem.2025.108424","url":null,"abstract":"<div><div>Cancer is a significant health and economic concern worldwide. Prostate cancer (PC) ranks as the fourth leading cause of global death and is the second most prevalent malignancy in males. Androgens are essential for the progress and growth of the prostate gland. PC is caused by androgens binding to receptors, which activates genes that promotes the development of PC. Nilutamide (NLM) is an antiandrogen medicine used in the treatment of PC. However, throughout treatment, it induces various toxicities and leads to resistance in patients. The objective of the work was to designed and evaluated safer NLM analogues using computational approaches with optimized pharmacokinetic profiles and less toxicity. Newer bioisosteres of the designed NLM analogues and their ADMET scores were calculated using the MolOpt and ADMETlab 3.0 tools, respectively. We conducted docking investigations of the designed ligands using AutoDock Vina software. The MolOpt web server produces 1575 bioisosteres of NLM using the scaffold transformation method. The 47 bioisosteres were selected based on pharmacokinetic profiles, drug likeness (DL) and drug score (DS) prediction scores and were determined to be optimum to excellent in comparison to NLM. The analogues NLM28, NLM31, NLM34, NLM38, NLM40, NLM44, NLM45, and NLM47 exhibited favorable interactions and docking scores with the protein (PDB ID: 2AM9). The molecular dynamics (MD) simulation results revealed that the NLM34 and NLM40 complexes were found stable during the 100 ns run. The findings indicate that the NLM analogues, particularly NLM34 and NLM40 have the potential to be used as promising antiandrogen agents for PC therapy.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108424"},"PeriodicalIF":2.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Machine Learning (ML) approach in discovery of novel drug targets against Leishmania: A computational based approach","authors":"Hayat Ali Shah ,&nbsp;Sabina Yasmin ,&nbsp;Mohammad Yousuf Ansari","doi":"10.1016/j.compbiolchem.2025.108423","DOIUrl":"10.1016/j.compbiolchem.2025.108423","url":null,"abstract":"<div><div>Molecules with potent anti-leishmanial activity play a crucial role in identifying treatments for leishmaniasis and aiding in the design of novel drugs to combat the disease, ultimately protecting individuals and populations. Various methods have been employed to represent molecular structures and predict effective anti-leishmanial molecules. However, each method faces challenges and limitations that must be addressed to optimize the drug discovery and design process. Recently, machine learning approaches have gained significant importance in overcoming the limitations of traditional methods across various fields. Therefore, there is an urgent need to build a computational pipeline using advanced machine learning and deep learning methods that help to predict anti-leishmanial activity of drug candidates. The proposed pipeline in this paper involves data collection, feature extraction, feature selection and prediction techniques. This review presents a comprehensive computational pipeline for anti-leishmanial drug discovery, highlighting its strengths, limitations, challenges, and future directions to improve treatment for this neglected tropical disease.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108423"},"PeriodicalIF":2.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing neighborhood topological indices for QSPR analysis of clinically approved immunosuppressive drugs in heart transplant therapy","authors":"R. Thamizhmaran ,&nbsp;G. Kalaimurugan ,&nbsp;Muhammad Kamran Siddiqui ,&nbsp;L. Vinnarasi ,&nbsp;A. Yuvaraj ,&nbsp;Muhammad Faisal Hanif","doi":"10.1016/j.compbiolchem.2025.108414","DOIUrl":"10.1016/j.compbiolchem.2025.108414","url":null,"abstract":"<div><div>Heart transplantation is a life-saving transplantation procedure for individuals with advanced heart failure who have gone through all other medicinal options. It is predicted that 5000 heart transplants will be performed annually worldwide. The immunosuppressive drugs are used after a heart transplant to prevent organ rejection. They may be administered both before and throughout the transplant process under specific circumstances. Quantitative Structure-Activity or Property Relationship using topological descriptors is essential in drug design since it allows one to anticipate the physicochemical characteristics of medications based on their molecular structure. This study investigates the neighborhood topological descriptors of immunosuppressive medications used to treat heart transplant patients. The highest predictive efficacy of the pharmaceuticals is demonstrated by the good association between the topological indicators and the physical characteristics of the transplant medications. Additionally, this data may be used by researchers to develop new and effective medications for recipients of heart transplants.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108414"},"PeriodicalIF":2.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}