Nonadditivity in public and inhouse data: implications for drug design

Journal of Cheminformatics

C. Margreitter¹  E. Nittinger²  C. Tyrchan²  D. Gogishvili³ 

[1] Computational Chemistry, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden;Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden;Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden;Department of Computer Science, Vrije Universiteit, De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands;

关键词: Nonadditivity analysis;    Structure-activity relationship;    Matched molecular pair analysis;    Experimental uncertainty;    Machine learning;    Support vector machine;    Random forest;   

DOI  :  10.1186/s13321-021-00525-z

来源: Springer

【 摘 要 】

Numerous ligand-based drug discovery projects are based on structure-activity relationship (SAR) analysis, such as Free-Wilson (FW) or matched molecular pair (MMP) analysis. Intrinsically they assume linearity and additivity of substituent contributions. These techniques are challenged by nonadditivity (NA) in protein–ligand binding where the change of two functional groups in one molecule results in much higher or lower activity than expected from the respective single changes. Identifying nonlinear cases and possible underlying explanations is crucial for a drug design project since it might influence which lead to follow. By systematically analyzing all AstraZeneca (AZ) inhouse compound data and publicly available ChEMBL25 bioactivity data, we show significant NA events in almost every second assay among the inhouse and once in every third assay in public data sets. Furthermore, 9.4% of all compounds of the AZ database and 5.1% from public sources display significant additivity shifts indicating important SAR features or fundamental measurement errors. Using NA data in combination with machine learning showed that nonadditive data is challenging to predict and even the addition of nonadditive data into training did not result in an increase in predictivity. Overall, NA analysis should be applied on a regular basis in many areas of computational chemistry and can further improve rational drug design.

【 授权许可】

CC BY