Electrostatic-field and surface-shape similarity for virtual screening and pose prediction

Abstract

We introduce a new method for rapid computation of 3D molecular similarity that combines electrostatic field comparison with comparison of molecular surface-shape and directional hydrogen-bonding preferences (called “eSim”). Rather than employing heuristic “colors” or user-defined molecular feature types to represent conformation-dependent molecular electrostatics, eSim calculates the similarity of the electrostatic fields of two molecules (in addition to shape and hydrogen-bonding). We present detailed virtual screening performance data on the standard 102 target DUD-E set. In its moderately fast screening mode, eSim running on a single computing core is capable of processing over 60 molecules per second. In this mode, eSim performed significantly better than all alternate methods for which full DUD-E data were available (mean ROC area of 0.74, p , by paired t-test, compared with the best performing alternate method). In addition, for 92 targets of the DUD-E set where multiple ligand-bound crystal structures were available, screening performance was assessed using alternate ligands or sets thereof (in their bound poses) as similarity targets. Using the joint alignment of five ligands for each protein target, mean ROC area exceeded 0.82 for the 92 targets. Design-focused application of ligand similarity methods depends on accurate predictions of geometric molecular relationships. We comprehensively assessed pose prediction accuracy by curating nearly 400,000 bound ligand pose pairs across the DUD-E targets. Overall, beginning from agnostic initial poses, we observed an 80% success rate for RMSD Å among the top 20 predicted eSim poses. These examples were split roughly 50/50 into cases with high direct atomic overlap (where a shared scaffold exists between a pair) and low direct atomic overlap (where where a ligand pair has dissimilar scaffolds but largely occupies the same space). Within the high direct atomic overlap subset, the pose prediction success rate was 93%. For the more challenging subset (where dissimilar scaffolds are to be aligned), the success rate was 70%. The eSim approach enables both large-scale screening and rational design of ligands and is rooted in physically meaningful, non-heuristic, molecular comparisons.

Optimized SQE atomic charges for peptides accessible via a web application

“…Partial atomic charges are real numbers assigned to individual atoms of a molecule that approximate the distribution of electron density among these atoms. Partial atomic charges find many applications in computational chemistry [ 1 – 3 ], chemoinformatics [ 4 – 6 ], bioinformatics [ 7 , 8 ], and nanoscience [ 9 , 10 ]. Because the charges are not physicochemical observables but a theoretical concept, many methods for their calculation have been developed.…”

Current Status of Baricitinib as a Repurposed Therapy for COVID-19

“…However, the structure of fedratinib suggests that it should have a different pharmacological profile compared to those of ruxolitinib and baricitinib. Some theoretical findings are validated through the reported biological activities associated with these drugs ( Table 1 ), [ 37 , 38 , 39 , 40 , 41 ]. In conclusion, both ruxolitinib and baracitinib may represent promising options for COVID-19 treatment; however, the clinically reported adverse effects of these drugs and our theoretical calculations, especially for fedratinib, raise an alarming concern regarding their safety.…”

“…Structural overlay between these molecules indicated that the similarity between ruxolitinib and baricitinib is 82.5%, whereas fedratinib highly deviates from ruxolitinib and baricitinib. Similarity and alignments were measured based on molecular fields descriptors generated by Cresset’s FieldAlign Software (version 1.0.2), ( Figure 5 ), [ 37 ]. This theoretical finding indicates that these two molecules might share similar pharmacological and adverse effects.…”

“…( C ) An overlay of fedratinib (Gray) on baricitinib (Green); electronic similarity percentage = 49.1%. Similarity and alignments were measured based on molecular fields descriptors generated by Cresset’s FieldAlign Software (version 1.0.2), [ 37 ].…”

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

“…These methods are subdivided into two categories: (1) alignment-free methods that are usually computationally faster because they do not require overlapping the molecules or evaluating properties related to the surface (Seddon et al, 2019 ) and (2) alignment-based methods that are computationally costly since these methods superimpose molecular shapes and analyze surface properties, such as polarity and hydrophobicity (Fontaine et al, 2007 ; Kumar and Zhang, 2018 ). Different methods have been used in the representation of the 3D molecular shape of the ligands, such as Gaussian overlay-based methods (Cai et al, 2013 ), atomic distance-based methods (Ballester et al, 2009 ; Ballester, 2011 ; Bonanno and Ebejer, 2020 ), and surface-based methods (Karaboga et al, 2013 ; Cleves et al, 2019 ). The recognized molecular shapes are transformed into the 3D molecular fingerprints that are then compared using similarities or distance indexes, such as Tanimoto, Dice, and Tversky coefficients (Shin et al, 2015 ).…”