Drug discovery is a continuously evolving area, essential for mankind albeit a very expensive process with a high attrition rate. The main challenge faced by pharmaceutical researchers today is to identify the major hurdles and validate the “developability” of a compound in the initial stages of drug development in order to select superior drug candidates with the best chances of success. This motivated us to introduce a “universal” approach for analyzing the pharmacodynamics, pharmacokinetics and toxicity profile of compounds based on the philosophy of QSAR. The present work deals with the development, validation and application of a novel QSPR formalism entitled EigenValue ANalySis (EVANS). This methodology encodes 3D structural information in terms of the atom pair distances along with molecular physicochemical properties to generate a set of unique hybrid descriptors, termed as “Eigenvalues.” The present article deals with the intricacies of the methodology and explores its applicability on a series of datasets for building pharmacodynamic QSAR models.

Microbial resistance to conventional antibiotics has led to a surge in antimicrobial peptide (AMP) rational design initiatives that rely heavily on algorithms with good prediction accuracy and sensitivity. We present a quantitative structure-activity relationship (QSAR) approach for predicting activity of cathelicidins, an AMP family with broad-spectrum activity. The best multiple linear regression model built against Escherichia coli ATCC 25922 could accurately predict activity of three rationally designed peptides CP, DP, and Mapcon, having high sequence similarity. On further experimental validation of the rationally designed peptides, CP was found to exhibit high antimicrobial activity with negligible hemolysis. Here, we provide CP, an AMP with potential therapeutic applications and a family-based QSAR model for AMP prediction.

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We present EigenValue ANalySis (EVANS), a QSPR methodology that considers 3D molecular information of enantiomeric ensembles of chiral molecules without the need to perform an alignment step. EVANS follows an intricate molecular modelling protocol that generates orthogonal eigenvalues from hybrid matrices of physicochemical properties and 3D structure; these eigenvalues are used as independent variables in QSPR analyses. The EVANS formalism has been presented and deployed to build quantitative structure pharmacokinetic relationship (QSPKR) models on a benchmark dataset for three critical PK parameters: steady-state volume of distribution (VDss), clearance (CL), and half-life (t1/2). Predictive QSPKR models were built by using the eigenvalues generated via the EVANS methodology in conjunction with multiple linear regression (MLR), random forest (RF), and support vector machine (SVM) algorithms, and it was observed that the EVANS QSPKR models sync with published work in the literature. Thus, we present the EVANS methodology as a first-line prediction tool to prioritise compounds in drug discovery and development.