Common Substructure Research Articles

Fragmenstein: predicting protein–ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology

Current strategies centred on either merging or linking initial hits from fragment-based drug design (FBDD) crystallographic screens generally do not fully leaverage 3D structural information. We show that an algorithmic approach (Fragmenstein) that ‘stitches’ the ligand atoms from this structural information together can provide more accurate and reliable predictions for protein–ligand complex conformation than general methods such as pharmacophore-constrained docking. This approach works under the assumption of conserved binding: when a larger molecule is designed containing the initial fragment hit, the common substructure between the two will adopt the same binding mode. Fragmenstein either takes the atomic coordinates of ligands from a experimental fragment screen and combines the atoms together to produce a novel merged virtual compound, or uses them to predict the bound complex for a provided molecule. The molecule is then energy minimised under strong constraints to obtain a structurally plausible conformer. The code is available at https://github.com/oxpig/Fragmenstein.Scientific contributionThis work shows the importance of using the coordinates of known binders when predicting the conformation of derivative molecules through a retrospective analysis of the COVID Moonshot data. This method has had a prior real-world application in hit-to-lead screening, yielding a sub-micromolar merger from parent hits in a single round. It is therefore likely to further benefit future drug design campaigns and be integrated in future pipelines.Graphical

Integrating machine learning and structural dynamics to explore B-cell lymphoma-2 inhibitors for chronic lymphocytic leukemia therapy.

Chronic lymphocytic leukemia (CLL) is a malignancy caused by the overexpression of the anti-apoptotic protein B-cell lymphoma-2 (BCL-2), making it a critical therapeutic target. This study integrates computational screening, molecular docking, and molecular dynamics to identify and validate novel BCL-2 inhibitors from the ChEMBL database. Starting with 836 BCL-2 inhibitors, we performed ADME and Lipinski's Rule of Five (RO5) filtering, clustering, maximum common substructure (MCS) analysis, and machine learning models (Random Forest, SVM, and ANN), yielding a refined set of 124 compounds. Among these, 13 compounds within the most common substructure (MCS1) cluster showed promising features and were prioritized. A docking-based re-evaluation highlighted four lead compounds-ChEMBL464268, ChEMBL480009, ChEMBL464440, and ChEMBL518858-exhibiting notable binding affinities. Although a reference molecule outperformed in docking, molecular dynamics (MD), and binding energy analyses, it failed ADME and Lipinski criteria, unlike the selected leads. Further validation through MD simulations and MM/GBSA energy calculations confirmed stable binding interactions for the leads, with ChEMBL464268 showing the highest stability and binding affinity (ΔGtotal = -80.35 ± 11.51kcal/mol). Free energy landscape (FEL) analysis revealed stable energy minima for these complexes, underscoring conformational stability. Despite moderate activity (pIC₅₀ values from 4.3 to 5.82), the favorable pharmacokinetic profiles of these compounds position them as promising BCL-2 inhibitor leads, with ChEMBL464268 emerging as the most promising candidate for further CLL therapeutic development.

Beta Sandwich-Like Folds: Sequences, Contacts, Classification of Invariant Substructures and Beta Sandwich Protein Grammar.

This chapter addresses the following fundamental question: Do sequences of protein domains with sandwich architecture have common sequence characteristics even though they belong to different superfamilies and folds? The analysis was carried out in two stages: (1) determination of domain substructures shared by all sandwich proteins and (2) detection of common sequence characteristics within the substructures. Analysis of supersecondary structures in domains of proteins revealed two types of four-strand substructures that are common to sandwich proteins. At least one of these common substructures was found in proteins of 42 sandwich-like folds (per structural classification in the CATH database). A comparison of sequence fragments and residue-residue contacts constituting common substructures revealed specific distributions of hydrophobic residues in these chains. The shared sequences and structural characteristics can be conceptualized as the "grammatical rules of beta protein linguistics." Understanding the structural and sequence commonalities of sandwich proteins may prove useful for rational protein design.

A Computational Approach Using α-Carbonic Anhydrase to Find Anti-Trypanosoma cruzi Agents.

Chagas disease has an ineffective drug treatment despite efforts made over the last four decades. The carbonic anhydrase of Trypanosoma cruzi (α-TcCA) has emerged as an interesting target for the design of new antiparasitic compounds due to its crucial role in parasite processes. The aim of this study was to identify potential α-TcCA inhibitors with trypanocide activity. A maximum common substructure (MCS) and molecular docking were used to carry out a ligand- and structure-based virtual screening of ZINC20 and MolPort databases. The compounds selected were evaluated in an in vitro model against the NINOA strain of Trypanosoma cruzi, and cytotoxicity was determined in a murine model of macrophage cells J774.2. Five sulfonamide derivatives (C7, C9, C14, C19, and C21) had the highest docking scores (-6.94 to -8.31 kcal/mol). They showed key residue interactions on the active site of the α-TcCA and good biopharmaceutical and pharmacokinetic properties. C7, C9, and C21 had half-maximal inhibitory concentration (IC50) values of 26, 61.6, and 49 μM, respectively, against NINOA strain epimastigotes of Trypanosoma cruzi. Compounds C7, C9, and C21 showed trypanocide activity; therefore, these results encourage the development of new trypanocidal agents based on their scaffold.

Input Pose is Key to Performance of Free Energy Perturbation: Benchmarking with Monoacylglycerol Lipase.

Free energy perturbation (FEP) methodologies have become commonplace methods for modeling potency in hit-to-lead and lead optimization stages of drug discovery. The conformational states of the initial poses of compounds for FEP+ calculations are often set up by alignment to a cocrystal structure ligand, but it is not clear if this method provides the best result for all proteins or all ligands. Not only are ligand conformational states potential variables in modeling compound potency in FEP but also the selection of crystallographic water molecules for inclusion in the FEP input structures can impact FEP models. Here, we report the results of FEP calculations using FEP+ from Schrödinger and starting from maximum common substructure alignment and docked poses generated with an array of docking methodologies. As a benchmark data set, we use monoacylglycerol lipase (MAGL), an important clinical drug target in cancer malignancy, neurological diseases, and metabolic disorders, and a set of 17 MAGL inhibitors. We found a large variation among FEP+ correlations to experimental IC50 values depending on the method used to generate the input pose and that the inclusion of ligand-based information in the docking process, with some methods, increases the correlation between FEP+ free energies and IC50 values. Upon analysis of the initial poses, we found that the differences in FEP+ correlations stemmed from rotation around a tertiary amide bond as well as translation of the compound toward the more hydrophobic side of the MAGL pocket. FEP+ estimation improved across all pose modeling methods when hydrogen bond constraint information was added. However, simple maximum common substructure alignment in the presence of all crystallographic water molecules outperformed all other methods in correlation between estimated and experimental IC50 values. Taken together, these findings suggest that pose selection and crystallographic water inclusion greatly impact how well FEP+ estimated IC50 values align with experimental IC50 values and that modelers should benchmark a few different pose generation methodologies and different water inclusion strategies for their hit-to-lead and lead optimization drug discovery projects.

Benchmarking Cross-Docking Strategies in Kinase Drug Discovery.

In recent years, machine learning has transformed many aspects of the drug discovery process, including small molecule design, for which the prediction of bioactivity is an integral part. Leveraging structural information about the interactions between a small molecule and its protein target has great potential for downstream machine learning scoring approaches but is fundamentally limited by the accuracy with which protein-ligand complex structures can be predicted in a reliable and automated fashion. With the goal of finding practical approaches to generating useful kinase-inhibitor complex geometries for downstream machine learning scoring approaches, we present a kinase-centric docking benchmark assessing the performance of different classes of docking and pose selection strategies to assess how well experimentally observed binding modes are recapitulated in a realistic cross-docking scenario. The assembled benchmark data set focuses on the well-studied protein kinase family and comprises a subset of 589 protein structures cocrystallized with 423 ATP-competitive ligands. We find that the docking methods biased by the cocrystallized ligand, utilizing shape overlap with or without maximum common substructure matching, are more successful in recovering binding poses than standard physics-based docking alone. Also, docking into multiple structures significantly increases the chance of generating a low root-mean-square deviation (RMSD) docking pose. Docking utilizing an approach that combines all three methods (Posit) into structures with the most similar cocrystallized ligands according to the maximum common substructure (MCS) proved to be the most efficient way to reproduce binding poses, achieving a success rate of 70.4% across all included systems. The studied docking and pose selection strategies, which utilize the OpenEye Toolkits, were implemented into pipelines of the KinoML framework, allowing automated and reliable protein-ligand complex generation for future downstream machine learning tasks. Although focused on protein kinases, we believe that the general findings can also be transferred to other protein families.

Repurposing Anidulafungin for Alzheimer's Disease via Fragment-Based Drug Discovery.

The misfolding and aggregation of beta-amyloid (Aβ) peptides have been implicated as key pathogenic events in the early stages of Alzheimer's disease (AD). Inhibiting Aβ aggregation represents a potential disease-modifying therapeutic approach to AD treatment. Previous studies have identified various molecules that inhibit Aβ aggregation, some of which share common chemical substructures (fragments) that may be key to their inhibitory activity. Employing fragment-based drug discovery (FBDD) methods may facilitate the identification of these fragments, which can subsequently be used to screen new inhibitors and provide leads for further drug development. In this study, we used an in silico FBDD approach to identify 17 fragment clusters that are significantly enriched among Aβ aggregation inhibitors. These fragments were then used to screen anti-infective agents, a promising drug class for repurposing against amyloid aggregation. This screening process identified 16 anti-infective drugs, 5 of which were chosen for further investigation. Among the 5 candidates, anidulafungin, an antifungal compound, showed high efficacy in inhibiting Aβ aggregation in vitro. Kinetic analysis revealed that anidulafungin selectively blocks the primary nucleation step of Aβ aggregation, substantially delaying Aβ fibril formation. Cell viability assays demonstrated that anidulafungin can reduce the toxicity of oligomeric Aβ on BV2 microglia cells. Molecular docking simulations predicted that anidulafungin interacted with various Aβ species, including monomers, oligomers, and fibrils, potentially explaining its activity against Aβ aggregation and toxicity. This study suggests that anidulafungin is a potential drug to be repurposed for AD, and FBDD is a promising approach for discovering drugs to combat Aβ aggregation.

Disk Evolution Study Through Imaging of Nearby Young Stars (DESTINYS): PDS 111, an old T Tauri star with a young-looking disk

The interplay between T\,Tauri stars and their circumstellar disks, and how this impacts the onset of planet formation has yet to be established. In the last years, major progress has been made using instrumentation that probes the dust structure in the mid-plane and at the surface of protoplanetary disks. Observations show a great variety of disk shapes and substructures that are crucial for understanding planet formation. We studied a seemingly old T\,Tauri star, PDS\,111, and its disk. We combined complementary observations of the stellar atmosphere, the circumstellar hot gas, the surface of the disk, and the mid-plane structure. We analyzed optical, infrared, and sub-millimeter observations obtained with VLT/X-shooter, Mercator/HERMES, TESS, VLT/SPHERE, and ALMA, providing a new view on PDS\,111 and its protoplanetary disk. The multi-epoch spectroscopy yields photospheric lines to classify the star and to update its stellar parameters, and emission lines to study variability in the hot inner disk and to determine the mass-accretion rate. The SPHERE and ALMA observations are used to characterize the dust distribution of the small and large grains, respectively. PDS\,111 is a weak-line T\,Tauri star with spectral type G2, exhibits strong Halpha variability and with a low mass-accretion rate of $1-5 odot $. We measured an age of the system of 15.9$^ $\,Myr using pre-main sequence tracks. The SPHERE observations show a strongly flaring disk with an asymmetric substructure. The ALMA observations reveal a 30\,au cavity in the dust continuum emission with a low contrast asymmetry in the South-West of the disk and a dust disk mass of 45.8\,$M_ or $ Jup $. The 12CO observations do not show a cavity and the 12CO radial extension is at least three times larger than that of the dust emission. Although the measured age is younger than often suggested in literature, PDS\,111 seems relatively old; this provides insight into disk properties at an advanced stage of pre-main sequence evolution. The characteristics of this disk are very similar to its younger counterparts: strongly flaring, an average disk mass, a typical radial extent of the disk gas and dust, and the presence of common substructures. This suggests that disk evolution has not significantly changed the disk properties. These results show similarities with the "Peter Pan disks" around M-dwarfs, that "refuse to evolve".

In the Beginning Was a to-Phrase

Abstract In this paper we reply to the objections raised against a connection between Double Object Constructions (DOC) and to-constructions and present new arguments showing the empirical and theoretical advantages of derivational analyses over non-derivational ones. We argue that to-constructions and DOCs share a common substructure—where the theme is higher than the goal—that construction-based analyses fail to capture, both crosslinguistically and English-internally. We also argue that variation on the lexical properties of verbs and adpositions is the right tool to account for the alternation.

RNAhugs web server for customized 3D RNA structure alignment.

Alignment of 3D molecular structures involves overlaying their sets of atoms in space in such a way as to minimize the distance between the corresponding atoms. The purpose of this procedure is usually to analyze and assess structural similarity on a global (e.g.evaluating predicted 3D models and clustering structures) or a local level (e.g.searching for common substructures). Although the idea of alignment is simple, combinatorial algorithms that implement it require considerable computational resources, even when processing relatively small structures. In this paper, we introduce RNAhugs, a web server for custom and flexible alignment of 3D RNA structures. Using two efficient heuristics, GEOS and GENS, it finds the longest corresponding fragments within 3D structures that may differ in sizes-given in the PDB or PDBx/mmCIF formats-that manage to align with user-specified accuracy (i.e.with an RMSD not exceeding a cutoff value given as an input parameter). A distinctive advantage of the system lies in its ability to process multi-model files and compare the results of 1-25 alignments in a single task. RNAhugs has an intuitive interface and is publicly available at https://rnahugs.cs.put.poznan.pl/.

Modeling and free vibration analysis of bolted composite flanged cylindrical-cylindrical shells under partial bolt loosening conditions

The bolted composite coupled cylindrical shell is a common substructure in modern engineering, which may encounter the issue of local loosening conditions during service. Meanwhile, the influence of flanges on the structural vibration characteristics is often overlooked in dynamic numerical analysis. In this paper, a semi-analytical dynamic model for the bolted composite flanged cylindrical-cylindrical shell structure under arbitrary connection conditions is established, and the free vibration characteristics of locally loose structures are emphatically studied. In the model, the annular plate modeling method is developed to achieve the simulation of the flange part. To fully reflect the connection characteristics of the bolted joint, a spring surface model and the corresponding parameter determination method based on fractal contact theory are developed. The equations of motion of the structure are obtained utilizing the Lagrange equations. The accuracy of the model and its predictive ability for the vibration characteristics of the bolted composite flanged cylindrical-cylindrical shell under local loosening conditions are fully demonstrated through numerous comparisons with experimental results from other literature and modal test results obtained under various working conditions. Furthermore, the influence of the number and degree of loosening bolts on the free vibration characteristics of the structure and the coupling phenomenon between different modes are discussed. The research in this paper is expected to provide references for the design of bolted composite flanged cylindrical shell structures and the monitoring of bolt connection defects.

Social life cycle assessment of railway track substructure alternatives

The sustainable design of infrastructure involves assessing economic, environmental, and social impacts. While significant progress has been made in evaluating economic and environmental life cycle impacts since the Paris Agreement, there's a notable gap in techniques for assessing social aspects in infrastructure design. This study introduces social indicators tailored for evaluating the lifecycle of railway infrastructures. The indicators are applied to assess the social impacts of three common railway track substructure solutions: conventional ballasted track, embedded slab track (BBEST solution), and sleeper-based, ballastless (RHEDA2000) substructure solutions. Using the Analytic Network Process (ANP), the social performance of each alternative is synthesized into a single indicator for comparison. Results indicate that the conventional ballasted track outperforms, scoring 12% higher than BBEST and 61% better than RHEDA in social terms. This is attributed to its reliable capacity for generating high-quality employment and fostering economic activities in the defined product system regions.

Repurposing Drugs for Senotherapeutic Effect: Potential Senomorphic Effects of Female Synthetic Hormones.

Repurposing previously approved drugs may fast track the route to the clinic for potential senotherapeutics and improves the inefficiency of the clinical drug development pipeline. We performed a repurposing screen of 240 clinically approved molecules in human primary dermal fibroblasts for their effects on CDKN2A expression. Molecules demonstrating effects on CDKN2A expression underwent secondary screening for senescence-associated beta galactosidase (SAB) activity, based on effect size, direction, and/or molecule identity. Selected molecules then underwent a more detailed assessment of senescence phenotypes including proliferation, apoptosis, DNA damage, senescence-associated secretory phenotype (SASP) expression, and regulators of alternative splicing. A selection of the molecules demonstrating effects on senescence were then used in a new bioinformatic structure-function screen to identify common structural motifs. In total, 90 molecules displayed altered CDKN2A expression at one or other dose, of which 15 also displayed effects on SAB positivity in primary human dermal fibroblasts. Of these, 3 were associated with increased SAB activity, and 11 with reduced activity. The female synthetic sex hormones-diethylstilboestrol, ethynyl estradiol and levonorgestrel-were all associated with a reduction in aspects of the senescence phenotype in male cells, with no effects visible in female cells. Finally, we identified that the 30 compounds that decreased CDKN2A activity the most had a common substructure linked to this function. Our results suggest that several drugs licensed for other indications may warrant exploration as future senotherapies, but that different donors and potentially different sexes may respond differently to senotherapeutic compounds. This underlines the importance of considering donor-related characteristics when designing drug screening platforms.

A Pd-Catalyzed Annulation Strategy to Linearly Fused Functionalized N-Heterocycles.

Linearly fused polycyclic piperidines represent common substructures in natural products and biologically active small molecules. We have devised a Pd-catalyzed annulation strategy to these compounds that converts readily available 2-tetralones and indanones into these scaffolds with the potential for control of both enantio- and diastereoselectivity. Importantly, these compounds can be chemoselectively functionalized, providing an efficient and robust methodology to these important nitrogen-containing molecules.

Chemical Multiverse and Diversity of Food Chemicals.

Food chemicals have a fundamental role in our lives, with an extended impact on nutrition, disease prevention, and marked economic implications in the food industry. The number of food chemical compounds in public databases has substantially increased in the past few years, which can be characterized using chemoinformatics approaches. We and other groups explored public food chemical libraries containing up to 26,500 compounds. This study aimed to analyze the chemical contents, diversity, and coverage in the chemical space of food chemicals and additives and, from here on, food components. The approach to food components addressed in this study is a public database with more than 70,000 compounds, including those predicted via omics techniques. It was concluded that food components have distinctive physicochemical properties and constitutional descriptors despite sharing many chemical structures with natural products. Food components, on average, have large molecular weights and several apolar structures with saturated hydrocarbons. Compared to reference databases, food component structures have low scaffold and fingerprint-based diversity and high structural complexity, as measured by the fraction of sp3 carbons. These structural features are associated with a large fraction of macronutrients as lipids. Lipids in food components were decompiled by an analysis of the maximum common substructures. The chemical multiverse representation of food chemicals showed a larger coverage of chemical space than natural products and FDA-approved drugs by using different sets of representations.

Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations.

Relative binding free energy (RBFE) calculations have emerged as a powerful tool that supports ligand optimization in drug discovery. Despite many successes, the use of RBFEs can often be limited by automation problems, in particular, the setup of such calculations. Atom mapping algorithms are an essential component in setting up automatic large-scale hybrid-topology RBFE calculation campaigns. Traditional algorithms typically employ a 2D subgraph isomorphism solver (SIS) in order to estimate the maximum common substructure. SIS-based approaches can be limited by time-intensive operations and issues with capturing geometry-linked chemical properties, potentially leading to suboptimal solutions. To overcome these limitations, we have developed Kartograf, a geometric-graph-based algorithm that uses primarily the 3D coordinates of atoms to find a mapping between two ligands. In free energy approaches, the ligand conformations are usually derived from docking or other previous modeling approaches, giving the coordinates a certain importance. By considering the spatial relationships between atoms related to the molecule coordinates, our algorithm bypasses the computationally complex subgraph matching of SIS-based approaches and reduces the problem to a much simpler bipartite graph matching problem. Moreover, Kartograf effectively circumvents typical mapping issues induced by molecule symmetry and stereoisomerism, making it a more robust approach for atom mapping from a geometric perspective. To validate our method, we calculated mappings with our novel approach using a diverse set of small molecules and used the mappings in relative hydration and binding free energy calculations. The comparison with two SIS-based algorithms showed that Kartograf offers a fast alternative approach. The code for Kartograf is freely available on GitHub (https://github.com/OpenFreeEnergy/kartograf). While developed for the OpenFE ecosystem, Kartograf can also be utilized as a standalone Python package.

Comprehensive, Open-Source, and Automated Workflow for Multisite λ-Dynamics in Lead Optimization.

Multisite λ-dynamics (MSLD) is a highly efficient binding free energy calculation method that samples multiple ligands in a single round by assigning different λ values to the alchemical part of each ligand. This method holds great promise for lead optimization (LO) in drug discovery. However, the complex data preparation and simulation process limits its widespread application in diverse protein-ligand systems. To address this challenge, we developed a comprehensive, open-source, and automated workflow for MSLD calculations based on the BLaDE dynamics engine. This workflow incorporates the Ligand Internal and Cartesian coordinate reconstruction-based alignment algorithm (LIC-align) and an optimized maximum common substructure (MCS) search algorithm to accurately generate MSLD multiple topologies with ideal perturbation patterns. Furthermore, our workflow is highly modularized, allowing straightforward integration and extension of various simulation techniques, and is highly accessible to nonexperts. This workflow was validated by calculating the relative binding free energies of large-scale congeneric ligands, many of which have large perturbing groups. The agreement between the calculations and experiments was excellent, with an average unsigned error of 1.08 ± 0.47 kcal/mol. More than 57.1% of the ligands had an error of less than 1.0 kcal/mol, and the perturbations of 6 targets were fully connected via the calculations, while those of 2 targets were connected via both calculations and experimental data. The Pearson correlation coefficient reached 0.88, indicating that the MSLD workflow provides accurate predictions that can guide lead optimization in drug discovery. We also examined the impact of single-site versus multisite perturbations, ligand grouping by perturbing group size, and the position of the anchor atom on the MSLD performance. By integrating our proposed LIC-align and optimized MCS search algorithm along with the coping strategies to handle challenging molecular substructures, our workflow can handle many realistic scenarios more reasonably than all previously published methods. Moreover, we observed that our MSLD workflow achieved similar accuracy to free energy perturbation (FEP) while improving computational efficiency by over 1 order of magnitude in speedup. These findings provide valuable insights and strategies for further MSLD development, making MSLD a competitive tool for lead optimization.

Orchid bee collects herbicide that mimics the fragrance of its orchid mutualists

Abstract Male orchid bees store volatile compounds collected from their orchid mutualists and other sources to use in their courtship. Males of a naturalized orchid bee in Florida, Euglossa dilemma Bembé & Eltz (Hymenoptera: Apidae), intensively and habitually collected from substrates impregnated with triclopyr herbicide, most probably collecting its major breakdown product TMP (3,5,6-trichloro-2-methoxypyridine). Why this occurred and if the practice harmed the bees was considered. The chemical is thought to have low toxicity to bees and orchid bees collect and manage volatile chemicals so that they do not contact the interior of their bodies, both suggesting limited harm from the practice. Molecular similarity comparisons of TMP with 24 volatile compounds collected by E. dilemma found greater than 50 % structural similarities in four compounds according to the maximum common substructure, suggesting that TMP mirrors compounds needed by the bee in its courtship, and probably explains why the bees collect triclopyr. The bizarre but interesting collection of an herbicide by this orchid bee appears to be due to the similarity between the herbicide and chemicals that the bee needs in its courtship. The herbicide does not appear to harm the bee.

Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations

Identifying bioactive conformations of small molecules is an essential process for virtual screening applications relying on three-dimensional structure such as molecular docking. For most small molecules, conformer generators retrieve at least one bioactive-like conformation, with an atomic root-mean-square deviation (ARMSD) lower than 1 Å, among the set of low-energy conformers generated. However, there is currently no general method to prioritise these likely target-bound conformations in the ensemble. In this work, we trained atomistic neural networks (AtNNs) on 3D information of generated conformers of a curated subset of PDBbind ligands to predict the ARMSD to their closest bioactive conformation, and evaluated the early enrichment of bioactive-like conformations when ranking conformers by AtNN prediction. AtNN ranking was compared with bioactivity-unaware baselines such as ascending Sage force field energy ranking, and a slower bioactivity-based baseline ranking by ascending Torsion Fingerprint Deviation to the Maximum Common Substructure to the most similar molecule in the training set (TFD2SimRefMCS). On test sets from random ligand splits of PDBbind, ranking conformers using ComENet, the AtNN encoding the most 3D information, leads to early enrichment of bioactive-like conformations with a median BEDROC of 0.29 ± 0.02, outperforming the best bioactivity-unaware Sage energy ranking baseline (median BEDROC of 0.18 ± 0.02), and performing on a par with the bioactivity-based TFD2SimRefMCS baseline (median BEDROC of 0.31 ± 0.02). The improved performance of the AtNN and TFD2SimRefMCS baseline is mostly observed on test set ligands that bind proteins similar to proteins observed in the training set. On a more challenging subset of flexible molecules, the bioactivity-unaware baselines showed median BEDROCs up to 0.02, while AtNNs and TFD2SimRefMCS showed median BEDROCs between 0.09 and 0.13. When performing rigid ligand re-docking of PDBbind ligands with GOLD using the 1% top-ranked conformers, ComENet ranked conformers showed a higher successful docking rate than bioactivity-unaware baselines, with a rate of 0.48 ± 0.02 compared to CSD probability baseline with a rate of 0.39 ± 0.02. Similarly, on a pharmacophore searching experiment, selecting the 20% top-ranked conformers ranked by ComENet showed higher hit rate compared to baselines. Hence, the approach presented here uses AtNNs successfully to focus conformer ensembles towards bioactive-like conformations, representing an opportunity to reduce computational expense in virtual screening applications on known targets that require input conformations.

Ligand-Based and Structure-Based Virtual Screening of New Sodium Glucose Cotransporter Type 2 Inhibitors.

Diabetes mellitus is a metabolic disease that causes multiple complications and common comorbidities, which decreases the quality of life for people affected by the disease. Sodium glucose cotransporter type 2 (SGLT2) reabsorbs 90% of glucose in the kidneys; therefore, it is an attractive drug target for controlling blood glucose levels. The aim of this work was to obtain new potential SGLT2 inhibitors. A ligand-based virtual screening (LBVS) from the ZINC15, PubChem and ChemSpider databases using the maximum common substructure (MCS) scaffold was performed. A total of 341 compounds were obtained and analyzed by molecular docking on the active site of SGLT2. Subsequently, 15 compounds were selected for molecular dynamics (MD) simulation analysis. The compounds derived from spiroketal Sa1, Sa4, and Sa9 (≤ 3.5 Å) in complex with the receptor SGLT2 showed good stability during 120 ns of MD. These compounds are proposed as potential SGLT2 inhibitors.