Abstract:Virtual Screening (VS) methods can drastically accelerate global drug discovery processes. Among the most widely used VS approaches, Shape Similarity Methods compare in detail the global shape of a query molecule against a large database of potential drug compounds. Even so, the databases are so enormously large that, in order to save time, the current VS methods are not exhaustive, but they are mainly local optimizers that can easily be entrapped in local optima. It means that they discard promising compounds… Show more
“…The benchmark covers a wide variety of pharmaceutically relevant targets (102 total), each with a curated protein structure and cognate bound ligand, along with a set of active ligands and computationally generated decoys. Two recent reports [19, 20] make use of the full set, as given, taking the DUD-E crystallographic ligand as the “query” in virtual screens, to benchmark molecular similarity methods in terms of both accuracy and speed. The similarity methods included cover widely-used methods such as ROCS [21, 22], other volumetric approaches (VAMS [19], WEGA [23], and OptiPharm [20]), and approaches that utilize conformer-specific features to characterize shape (USR [24]).Fig.…”
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.
“…The benchmark covers a wide variety of pharmaceutically relevant targets (102 total), each with a curated protein structure and cognate bound ligand, along with a set of active ligands and computationally generated decoys. Two recent reports [19, 20] make use of the full set, as given, taking the DUD-E crystallographic ligand as the “query” in virtual screens, to benchmark molecular similarity methods in terms of both accuracy and speed. The similarity methods included cover widely-used methods such as ROCS [21, 22], other volumetric approaches (VAMS [19], WEGA [23], and OptiPharm [20]), and approaches that utilize conformer-specific features to characterize shape (USR [24]).Fig.…”
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.
“…VS applied to the electrostatic similarity of compounds is a clear example of this. Contrary to what happens when VS is applied to select the most similar compounds in shape or pharmacophore properties, where the tools base their predictions on scoring functions that measure these particular features (Lešnik et al, 2015;Puertas-Martín et al, 2019;Yan et al, 2013), the predictions in this field are not exclusively based on this descriptor, but on both the similarity of the three dimensional shape and electrostatic similarity (Tresadern et al, 2009;Chu and Gochin, 2013;Kim et al, 2015;Kossmann et al, 2016;Woodring et al, 2017;Maccari et al, 2011;Kim et al, 2016;López-Ramos and Perruccio, 2010;Hevener et al, 2012;Kaoud et al, 2012;Tiikkainen et al, 2009;Massarotti et al, 2014;Oyarzabal et al, 2009).…”
Section: Introductionmentioning
confidence: 99%
“…To do so, a new version of the algorithm OptiPharm, called OptiPharm_ES, has been implemented. OptiPharm (Puertas-Martín et al, 2019) was initially designed to optimize the shape similarity between two given molecules, but now it has been adapted to maximize the electrostatic similarity. As results will show, the new LBVS-Electrostatic methodology is able to obtain better solutions than the ones obtained with the classical LBVS-Shape approach.…”
Ligand Based Virtual Screening methods are widely used in drug discovery as filters for subsequent in-vitro and in-vivo characterization. Since the databases processed are enormously large, this pre-selection process requires the use of fast and precise methodologies. In this work, the similarity between compounds is measured in terms of electrostatic potential. To do so, we propose a new and alternative methodology, called LBVS-Electrostatic. Accordingly to the obtained results, we are able to conclude that many of the compounds proposed with our novel approach could not be discovered with the classical one.
“…12 VS applied to the electrostatic similarity of compounds is a clear example of this. Contrary to what happens when VS is applied to select the most similar compounds in shape or pharmacophore properties, where the tools base their predictions on scoring functions that measure these particular features, 7,13,14 the predictions in this field are not exclusively based on this descriptor, but on both the similarity of the three dimensional shape and electrostatic similarity. [15][16][17][18][19][20][21][22][23][24][25][26][27] Broadly speaking, all the previous works follow the same methodology, although they may differ in the selection procedure used to determine the compounds proposed as best predictions.…”
Section: Introductionmentioning
confidence: 99%
“…It means OptiPharm can be adapted to different sorts of problems which is an advantage regarding the virtual screening problem, where compounds have different sizes and complexities. The interested reader is referred to the original work 13 for an in-depth description of this method. Additionally, they have software available with the web tool BRUSELAS 29 (http://bio-hpc.eu/software/Bruselas).…”
<div>Ligand Based Virtual Screening (LBVS) methods are widely used in drug discovery as filters for subsequent in-vitro and in-vivo characterization. This means, increasing accuracy of LBVS approaches may have a huge impact on increasing chances of success. Since the databases processed in drug discovery campaigns are enormously large, this pre-selection process requires the use of fast and precise methodologies. The similarity between compounds can be measured using different descriptors such as shape, pharmacophore or electrostatic similarity. The latter is the goal of this work, i.e., we want to improve the process of obtaining the compounds most similar to a query in terms of electrostatic similarity. To do so, the current and widely proposed methodology in the literature is based on the use of ROCS to assess the similarity of compounds in terms of shape and then evaluate a small subset of them with ZAP for prioritization regarding electrostatic similarity. This paper proposes an alternative methodology that consists of directly optimizing electrostatic similarity and works with the entire database of compounds without using shape cut-offs. For this purpose, a new and improved version of the OptiPharm software has been developed. OptiPharm implements a parameterizable metaheuristic algorithm able to solve any optimization problems directly related to the involved molecular conformations. We show that our new method completely outperforms the classical proposal widely used in the literature. Accordingly, we are able to conclude that many of the compounds proposed with our novel approach could not be discovered with the classical one. As a result, this methodology opens up new horizons in Drug Discovery.</div>
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