Exploring non-linear distance metrics in the structure–activity space: QSAR models for human estrogen receptor

Abstract

Abstract Background Quantitative structure-activity relationship (QSAR) models are important tools used in discovering new drug candidates and identifying potentially harmful environmental chemicals. These models often face two fundamental challenges: limited amount of available biological activity data and noise or uncertainty in the activity data themselves. To address these challenges, we introduce and explore a QSAR model based on custom distance metrics in the structure-activity space. Methods The model is built on top of the k-nearest neighbor model, incorporating non-linearity not only in the chemical structure space, but also in the biological activity space. The model is tuned and evaluated using activity data for human estrogen receptor from the US EPA ToxCast and Tox21 databases. Results The model closely trails the CERAPP consensus model (built on top of 48 individual human estrogen receptor activity models) in agonist activity predictions and consistently outperforms the CERAPP consensus model in antagonist activity predictions. Discussion We suggest that incorporating non-linear distance metrics may significantly improve QSAR model performance when the available biological activity data are limited.

Keywords

• Chemical space
• Molecular similarity
• Distance metrics
• Structure–activity landscape
• QSAR models
• Human estrogen receptor