Georgy S Malakhov, Dmitry A Karasev, Boris N Sobolev
{"title":"Comparative efficiency of structure activity relationship and proteochemometric modelling.","authors":"Georgy S Malakhov, Dmitry A Karasev, Boris N Sobolev","doi":"10.1016/j.jmgm.2025.109134","DOIUrl":null,"url":null,"abstract":"<p><p>Virtual screening of biologically active compounds is widely applied for the search of drug leads. The well-known methods of structure-activity relationship (SAR) are based on the chemical structure comparison. In the last years, an approach known as proteochemometrics (PCM) has also gained popularity. PCM extends the capabilities of SAR by incorporating the protein target descriptors into the model. Unlike SAR, PCM can be used to predict new targets with unknown spectra of ligands. As both approaches can be used to predict ligands for the known proteins, several researchers apply PCM to solve this task, without providing compelling reasons to support the superiority of the PCM approach over SAR. To correctly compare the performance of SAR and PCM in the given situation, we have developed a special validation scheme. As a result, we did not find any advantages of PCM over SAR in the prediction of ligands for the protein with an established ligand spectrum. At the same time, the validation procedure commonly used for PCM models considerably inflates the evaluation scores compared to our technique. Widespread use of such validation scheme leads to conclusions that PCM has great advantage over SAR in contrast to our findings. Thus, our study emphasizes that a transparent and correct validation scheme is essential for comparison of different methods.</p>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"141 ","pages":"109134"},"PeriodicalIF":3.0000,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of molecular graphics & modelling","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.jmgm.2025.109134","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/8/6 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Virtual screening of biologically active compounds is widely applied for the search of drug leads. The well-known methods of structure-activity relationship (SAR) are based on the chemical structure comparison. In the last years, an approach known as proteochemometrics (PCM) has also gained popularity. PCM extends the capabilities of SAR by incorporating the protein target descriptors into the model. Unlike SAR, PCM can be used to predict new targets with unknown spectra of ligands. As both approaches can be used to predict ligands for the known proteins, several researchers apply PCM to solve this task, without providing compelling reasons to support the superiority of the PCM approach over SAR. To correctly compare the performance of SAR and PCM in the given situation, we have developed a special validation scheme. As a result, we did not find any advantages of PCM over SAR in the prediction of ligands for the protein with an established ligand spectrum. At the same time, the validation procedure commonly used for PCM models considerably inflates the evaluation scores compared to our technique. Widespread use of such validation scheme leads to conclusions that PCM has great advantage over SAR in contrast to our findings. Thus, our study emphasizes that a transparent and correct validation scheme is essential for comparison of different methods.
期刊介绍:
The Journal of Molecular Graphics and Modelling is devoted to the publication of papers on the uses of computers in theoretical investigations of molecular structure, function, interaction, and design. The scope of the journal includes all aspects of molecular modeling and computational chemistry, including, for instance, the study of molecular shape and properties, molecular simulations, protein and polymer engineering, drug design, materials design, structure-activity and structure-property relationships, database mining, and compound library design.
As a primary research journal, JMGM seeks to bring new knowledge to the attention of our readers. As such, submissions to the journal need to not only report results, but must draw conclusions and explore implications of the work presented. Authors are strongly encouraged to bear this in mind when preparing manuscripts. Routine applications of standard modelling approaches, providing only very limited new scientific insight, will not meet our criteria for publication. Reproducibility of reported calculations is an important issue. Wherever possible, we urge authors to enhance their papers with Supplementary Data, for example, in QSAR studies machine-readable versions of molecular datasets or in the development of new force-field parameters versions of the topology and force field parameter files. Routine applications of existing methods that do not lead to genuinely new insight will not be considered.