Conformations and 3D pharmacophore searching

Unveiling potential PET degrading eukaryotes through in silico bioprospecting of PETases

This study addresses the environmental problem of PET plastic through in silico bioprospecting for the identification and experimental validation of novel PET degrading eukaryotes through the in silico bioprospectingI of PETases, employing a methodology that combines Hidden Markov Models (HMMs), clustering techniques, molecular docking, and dynamic simulations. A total of 424 putative PETase sequences were identified from 219 eukaryotic organisms, highlighting six sequences with low affinity energies. The Aspergillus luchuensis sequence showed the lowest Gibbs free energy and exhibited stability at different temperatures in molecular dynamics assays. Experimental validation, through a plate clearance assay and HPLC, confirmed PETase activity in three wild-type fungal strains, with A. luchuensis showing the highest efficiency. The results obtained demonstrate the effectiveness of combining computational and experimental approaches as proof of concept to discover and validate eukaryotes with PET-degrading capabilities opening new perspectives for the sustainable management of this type of waste and contributing to its environmental mitigation.

A variant on the CREST iMTD algorithm for noncovalent clusters of flexible molecules

Conformational ensemble generation and the search for the global minimum conformation are important problems in computational chemistry. In this work, a variant on the conformer-rotamer ensemble sampling tool (CREST) iterative metadynamics (iMTD) algorithm designed for determining structural ensembles and energetics of noncovalent clusters of flexible molecules is presented. We term this new algorithm a low-energy diversity-enhanced variant on CREST, or LEDE-CREST. As with CREST, the energies are evaluated using the semiempirical GFN2-xTB extended tight binding approach. The utility of the algorithm is highlighted by generating ensembles for a variety of noncovalent clusters of flexible or rigid monomers using both CREST and LEDE-CREST.

Efficient molecular conformation generation with quantum-inspired algorithm

CONTEXT

Conformation generation, also known as molecular unfolding (MU), is a crucial step in structure-based drug design, remaining a challenging combinatorial optimization problem. Quantum annealing (QA) has shown great potential for solving certain combinatorial optimization problems over traditional classical methods such as simulated annealing (SA). However, a recent study showed that a 2000-qubit QA hardware was still unable to outperform SA for the MU problem. Here, we propose the use of quantum-inspired algorithm to solve the MU problem, in order to go beyond traditional SA. We introduce a highly compact phase encoding method which can exponentially reduce the representation space, compared with the previous one-hot encoding method. For benchmarking, we tested this new approach on the public QM9 dataset generated by density functional theory (DFT). The root-mean-square deviation between the conformation determined by our approach and DFT is negligible (less than about 0.5Å), which underpins the validity of our approach. Furthermore, the median time-to-target metric can be reduced by a factor of five compared to SA. Additionally, we demonstrate a simulation experiment by MindQuantum using quantum approximate optimization algorithm (QAOA) to reach optimal results. These results indicate that quantum-inspired algorithms can be applied to solve practical problems even before quantum hardware becomes mature.

METHODS

The objective function of MU is defined as the sum of all internal distances between atoms in the molecule, which is a high-order unconstrained binary optimization (HUBO) problem. The degree of freedom of variables is discretized and encoded with binary variables by the phase encoding method. We employ the quantum-inspired simulated bifurcation algorithm for optimization. The public QM9 dataset is generated by DFT. The simulation experiment of quantum computation is implemented by MindQuantum using QAOA.

Screening of bioactive compounds from selected mushroom species against putative drug targets in Mycobacterium tuberculosis: a multi-target approach

Mycobacterium tuberculosis (Mtb) is a notorious pathogen that causes one of the highest mortalities globally. Due to a pressing demand to identify novel therapeutic alternatives, the present study aims to focus on screening the putative drug targets and prioritizing their role in antibacterial drug development. The most vital proteins involved in the Biotin biosynthesis pathway and the Lipoarabinomannan (LAM) pathway such as biotin synthase (bioB) and alpha-(1->6)-mannopyranosyltransferase A (mptA) respectively, along with other essential virulence proteins of Mtb were selected as drug targets. Among these, the ones without native structures were modelled and validated using standard bioinformatics tools. Further, the interactions were performed with naturally available lead molecules present in selected mushroom species such as Agaricus bisporus, Pleurotus djamor, Hypsizygus ulmarius. Through Gas Chromatography-Mass Spectrometry (GC-MS), 15 bioactive compounds from the methanolic extract of mushrooms were identified. Further, 4 were selected based on drug-likeness and pharmacokinetic screening for molecular docking analysis against our prioritized targets wherein Benz[e]azulene from Pleurotus djamor illustrated a good binding affinity with a LF rank score of -9.036 kcal mol -1 against nuoM (NADH quinone oxidoreductase subunit M) and could be used as a prospective candidate in order to combat Tuberculosis (TB). Furthermore, the stability of the complex are validated using MD Simulations and subsequently, the binding free energy was calculated using MM-GBSA analysis. Thus, the current in silico analysis suggests a promising role of compounds extracted from mushrooms in tackling the TB burden.Communicated by Ramaswamy H. Sarma.

Insilico screening to identify novel inhibitors targeting 30S ribosomal protein S12 in meningitis-causing organism 'Elizabethkingia meningoseptica'

The current trend in biomedical research is on prioritizing infections based on multidrug resistance. Elizabethkingia meningoseptica, a nosocomial infection-causing organism emerging from Neonatal Intensive Care Units (NICUs), leads to neonatal meningitis and sepsis resulting in severe illness, and, in some cases, fatal. Finding a solution remains challenging due to limited prior work. Translational S12 ribosomal proteins play a crucial role in decoding the codon-anticodon helix, which is essential for the survival of E. meningoseptica. These proteins do not exhibit significant similarity with humans, making them potential drug targets. An in silico study aims to identify specific inhibitors for E. meningoseptica ribosomal proteins among known bioactive compounds targeting prokaryotic 30S ribosomal protein. A 3D model of the 7JIL_h protein from Flavobacterium johnsoniae, showing 90% sequence similarity with the target protein was generated using SWISS-MODEL software. The model was validated through Molprobity v4.4, VERIFY 3D, Errata, and ProSA analysis, confirming conserved residues of the target protein. Insilico screening of known bioactive compounds and their analogs identified potential ligands for the target protein. Molecular Docking and post-docking analysis assessed the stability of the protein-ligand complexes among the shortlisted compounds. The top two compounds with high Gold fitness scores and low predicted binding energy underwent MD simulation and further estimation of free binding energy using the MM_PBSA module. These computationally shortlisted compounds, namely chEMBL 1323619 and chEMBL 312490 may be considered for future in-vivo studies as potential inhibitors against the modeled 30S ribosomal protein S12 of E. meningoseptica.Communicated by Ramaswamy H. Sarma.

Molecular Gas-Phase Conformational Ensembles

Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.

Design, synthesis and preclinical evaluation of muscarine receptor antagonists via a scaffold-hopping approach

Our research group recently identified a rearrangement product of pirenzepine as starting point for a comprehensive rational drug design approach towards orthosteric muscarinic acetylcholine receptor ligands. Chemical reduction and bioscaffold hop lead to the development of sixteen promising compounds featuring either a benzimidazole or carbamate moiety, all exhibiting comparable pharmacophoric characteristics. The synthesized compounds were characterized by NMR, HR-MS, and RP-HPLC techniques. Subsequent evaluation encompassed binding affinity assessment on CHO-hM1-5 cells, mode of action determination, and analysis of physico-chemical parameters. The CNS MPO score indicated favorable drug-like attributes and potential CNS activity for the antagonistic ligands. The most promising compounds displayed Ki-values within a desirable low nanomolar range, and their structural features allow for potential carbon-11 radiolabeling. Our optimization efforts resulted in compounds with a remarkable 138-fold increase in binding affinity compared to the previously mentioned rearrangement product towards human M5, suggesting their prospective utility in positron emission tomography applications.

Assessing the fast prediction of peptide conformers and the impact of non-natural modifications

We present an assessment of different approaches to predict peptide structures using modeling tools. Several small molecule, protein, and peptide-focused methodologies were used for the fast prediction of conformers for peptides shorter than 30 amino acids. We assessed the effect of including restraints based on annotated or predicted secondary structure motifs. A number of peptides in bound conformations and in solution were collected to compare the tools. In addition, we studied the impact of changing single amino acids to non-natural residues using molecular dynamics simulations. Deep learning methods such as AlphaFold2, or the combination of physics-based approaches with secondary structure information, produce the most accurate results for natural sequences. In the case of peptides with non-natural modifications, modeling the peptide containing natural amino acids first and then modifying and simulating the peptide using benchmarked force fields is a recommended pipeline. The results can guide the modeling of oligopeptides for drug discovery projects.

Conformer Generation for Structure-Based Drug Design: How Many and How Good?

Conformer generation, the assignment of realistic 3D coordinates to a small molecule, is fundamental to structure-based drug design. Conformational ensembles are required for rigid-body matching algorithms, such as shape-based or pharmacophore approaches, and even methods that treat the ligand flexibly, such as docking, are dependent on the quality of the provided conformations due to not sampling all degrees of freedom (e.g., only sampling torsions). Here, we empirically elucidate some general principles about the size, diversity, and quality of the conformational ensembles needed to get the best performance in common structure-based drug discovery tasks. In many cases, our findings may parallel "common knowledge" well-known to practitioners of the field. Nonetheless, we feel that it is valuable to quantify these conformational effects while reproducing and expanding upon previous studies. Specifically, we investigate the performance of a state-of-the-art generative deep learning approach versus a more classical geometry-based approach, the effect of energy minimization as a postprocessing step, the effect of ensemble size (maximum number of conformers), and construction (filtering by root-mean-square deviation for diversity) and how these choices influence the ability to recapitulate bioactive conformations and perform pharmacophore screening and molecular docking.

SILVR: Guided Diffusion for Molecule Generation

Computationally generating new synthetically accessible compounds with high affinity and low toxicity is a great challenge in drug design. Machine learning models beyond conventional pharmacophoric methods have shown promise in the generation of novel small-molecule compounds but require significant tuning for a specific protein target. Here, we introduce a method called selective iterative latent variable refinement (SILVR) for conditioning an existing diffusion-based equivariant generative model without retraining. The model allows the generation of new molecules that fit into a binding site of a protein based on fragment hits. We use the SARS-CoV-2 main protease fragments from Diamond XChem that form part of the COVID Moonshot project as a reference dataset for conditioning the molecule generation. The SILVR rate controls the extent of conditioning, and we show that moderate SILVR rates make it possible to generate new molecules of similar shape to the original fragments, meaning that the new molecules fit the binding site without knowledge of the protein. We can also merge up to 3 fragments into a new molecule without affecting the quality of molecules generated by the underlying generative model. Our method is generalizable to any protein target with known fragments and any diffusion-based model for molecule generation.

A Novel Therapeutic Formulation for the Improved Treatment of Indian Red Scorpion (Mesobuthus tamulus) Venom-Induced Toxicity-Tested in Caenorhabditis elegans and Rodent Models

Indian Red Scorpion (Mesobuthus tamulus) stings are a neglected public health problem in tropical and sub-tropical countries, including India. The drawbacks of conventional therapies using commercial anti-scorpion antivenom (ASA) and α1-adrenoreceptor antagonists (AAA) have prompted us to search for an adequate formulation to improve treatment against M. tamulus stings. Novel therapeutic drug formulations (TDF) of low doses of commercial ASA, AAA, and ascorbic acid have remarkably improved in neutralising the in vivo toxic effects of M. tamulus venom (MTV) tested in Caenorhabditis elegans and Wistar strain albino rats in vivo models. The neutralisation of MTV-induced production of free radicals, alteration of the mitochondrial transmembrane potential, and upregulated expression of genes involved in apoptosis, detoxification, and stress response in C. elegans by TDF surpassed the same effect shown by individual components of the TDF. Further, TDF efficiently neutralized the MTV-induced increase in blood glucose level within 30 to 60 min post-treatment, organ tissue damage, necrosis, and pulmonary oedema in Wistar rats, indicating its clinical application for effecting treating M. tamulus envenomation. This study demonstrates for the first time that C. elegans can be a model organism for screening the neutralization potency of the drug molecules against a neurotoxic scorpion venom.

Sclerostin small-molecule inhibitors promote osteogenesis by activating canonical Wnt and BMP pathways

Background

The clinical healing environment after a posterior spinal arthrodesis surgery is one of the most clinically challenging bone-healing environments across all orthopedic interventions due to the absence of a contained space and the need to form de novo bone. Our group has previously reported that sclerostin in expressed locally at high levels throughout a developing spinal fusion. However, the role of sclerostin in controlling bone fusion remains to be established.

Methods

We computationally identified two FDA-approved drugs, as well as a single novel small-molecule drug, for their ability to disrupt the interaction between sclerostin and its receptor, LRP5/6. The drugs were tested in several in vitro biochemical assays using murine MC3T3 and MSCs, assessing their ability to (1) enhance canonical Wnt signaling, (2) promote the accumulation of the active (non-phosphorylated) form of β-catenin, and (3) enhance the intensity and signaling duration of BMP signaling. These drugs were then tested subcutaneously in rats as standalone osteoinductive agents on plain collagen sponges. Finally, the top drug candidates (called VA1 and C07) were tested in a rabbit posterolateral spine fusion model for their ability to achieve a successful fusion at 6 wk.

Results

We show that by controlling GSK3b phosphorylation our three small-molecule inhibitors (SMIs) simultaneously enhance canonical Wnt signaling and potentiate canonical BMP signaling intensity and duration. We also demonstrate that the SMIs produce dose-dependent ectopic mineralization in vivo in rats as well as significantly increase posterolateral spine fusion rates in rabbits in vivo, both as standalone osteogenic drugs and in combination with autologous iliac crest bone graft.

Conclusions

Few if any osteogenic small molecules possess the osteoinductive potency of BMP itself - that is, the ability to form de novo ectopic bone as a standalone agent. Herein, we describe two such SMIs that have this unique ability and were shown to induce de novo bone in a stringent in vivo environment. These SMIs may have the potential to be used in novel, cost-effective bone graft substitutes for either achieving spinal fusion or in the healing of critical-sized fracture defects.

Funding

This work was supported by a Veteran Affairs Career Development Award (IK2-BX003845).

Key essential oil components delocalize Candida albicans Kar3p and impact microtubule structure

BACKGROUND

Treatment of Candida albicans associated infections is often ineffective in the light of resistance, with an urgent need to discover novel antimicrobials. Fungicides require high specificity and can contribute to antifungal resistance, so inhibition of fungal virulence factors is a good strategy for developing new antifungals.

OBJECTIVES

Examine the impact of four plant-derived essential oil components (1,8-cineole, α-pinene, eugenol, and citral) on C. albicans microtubules, kinesin motor protein Kar3 and morphology.

METHODS

Microdilution assays were used to determine minimal inhibitory concentrations, microbiological assays assessed germ tube, hyphal and biofilm formation, confocal microscopy probed morphological changes and localization of tubulin and Kar3p, and computational modelling was used to examine the theoretical binding of essential oil components to tubulin and Kar3p.

RESULTS

We show for the first time that essential oil components delocalize the Kar3p, ablate microtubules, and induce psuedohyphal formation with reduced biofilm formation. Single and double deletion mutants of kar3 were resistant to 1,8-cineole, sensitive to α-pinene and eugenol, but unimpacted by citral. Strains with homozygous and heterozygous Kar3p disruption had a gene-dosage effect for all essential oil components, resulting in enhanced resistance or susceptibility patterns that were identical to that of cik1 mutants. The link between microtubule (αβ-tubulin) and Kar3p defects was further supported by computational modeling, showing preferential binding to αβ-tubulin and Kar3p adjacent to their Mg²⁺-binding sites.

CONCLUSION

This study highlights how essential oil components interfere with the localization of the kinesin motor protein complex Kar3/Cik1 and disrupt microtubules, leading to their destabilization which results in hyphal and biofilm defects.

Application of computational methods for class A GPCR Ligand discovery

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.

SDEGen: learning to evolve molecular conformations from thermodynamic noise for conformation generation

Generation of representative conformations for small molecules is a fundamental task in cheminformatics and computer-aided drug discovery, but capturing the complex distribution of conformations that contains multiple low energy minima is still a great challenge. Deep generative modeling, aiming to learn complex data distributions, is a promising approach to tackle the conformation generation problem. Here, inspired by stochastic dynamics and recent advances in generative modeling, we developed SDEGen, a novel conformation generation model based on stochastic differential equations. Compared with existing conformation generation methods, it enjoys the following advantages: (1) high model capacity to capture multimodal conformation distribution, thereby searching for multiple low-energy conformations of a molecule quickly, (2) higher conformation generation efficiency, almost ten times faster than the state-of-the-art score-based model, ConfGF, and (3) a clear physical interpretation to learn how a molecule evolves in a stochastic dynamics system starting from noise and eventually relaxing to the conformation that falls in low energy minima. Extensive experiments demonstrate that SDEGen has surpassed existing methods in different tasks for conformation generation, interatomic distance distribution prediction, and thermodynamic property estimation, showing great potential for real-world applications.

Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity

Lipophilicity, as measured by the partition coefficient between octanol and water (log P), is a key parameter in early drug discovery research. However, measuring log P experimentally is difficult for specific compounds and log P ranges. The resulting lack of reliable experimental data impedes development of accurate in silico models for such compounds. In certain discovery projects at Novartis focused on such compounds, a quantum mechanics (QM)-based tool for log P estimation has emerged as a valuable supplement to experimental measurements and as a preferred alternative to existing empirical models. However, this QM-based approach incurs a substantial computational cost, limiting its applicability to small series and prohibiting quick, interactive ideation. This work explores a set of machine learning models (Random Forest, Lasso, XGBoost, Chemprop, and Chemprop3D) to learn calculated log P values on both a public data set and an in-house data set to obtain a computationally affordable, QM-based estimation of drug lipophilicity. The message-passing neural network model Chemprop emerged as the best performing model with mean absolute errors of 0.44 and 0.34 log units for scaffold split test sets of the public and in-house data sets, respectively. Analysis of learning curves suggests that a further decrease in the test set error can be achieved by increasing the training set size. While models directly trained on experimental data perform better at approximating experimentally determined log P values than models trained on calculated values, we discuss the potential advantages of using calculated log P values going beyond the limits of experimental quantitation. We analyze the impact of the data set splitting strategy and gain insights into model failure modes. Potential use cases for the presented models include pre-screening of large compound collections and prioritization of compounds for full QM calculations.

BIreactive: Expanding the Scope of Reactivity Predictions to Propynamides

We present the first comprehensive study on the prediction of reactivity for propynamides. Covalent inhibitors like propynamides often show improved potency, selectivity, and unique pharmacologic properties compared to their non-covalent counterparts. In order to achieve this, it is essential to tune the reactivity of the warhead. This study shows how three different in silico methods can predict the in vitro properties of propynamides, a covalent warhead class integrated into approved drugs on the market. Whereas the electrophilicity index is only applicable to individual subclasses of substitutions, adduct formation and transition state energies have a good predictability for the in vitro reactivity with glutathione (GSH). In summary, the reported methods are well suited to estimate the reactivity of propynamides. With this knowledge, the fine tuning of the reactivity is possible which leads to a speed up of the design process of covalent drugs.

Introducing ligand GA, a genetic algorithm molecular tool for automated protein inhibitor design

Ligand GA is introduced in this work and approaches the problem of finding small molecules inhibiting protein functions by using the protein site to find close to optimal or optimal small molecule binders. Genetic algorithms (GA) are an effective means for approximating or solving computationally hard mathematics problems with large search spaces such as this one. The algorithm is designed to include constraints on the generated molecules from ADME restriction, localization in a binding site, specified hydrogen bond requirements, toxicity prevention from multiple proteins, sub-structure restrictions, and database inclusion. This algorithm and work is in the context of computational modeling, ligand design and docking to protein sites.

Interaction of the Emerging Mycotoxins Beauvericin, Cyclopiazonic Acid, and Sterigmatocystin with Human Serum Albumin

Beauvericin (BEA), cyclopiazonic acid (CPA), and sterigmatocystin (STC) are emerging mycotoxins. They appear as contaminants in food and animal feed, leading to economic losses and health risks. Human serum albumin (HSA) forms stable complexes with certain mycotoxins, including ochratoxins, alternariol, citrinin, and zearalenone. HSA binding can influence the toxicokinetics of xenobiotics, and albumin can also be considered and applied as a relatively cheap affinity protein. Therefore, we examined the potential interactions of BEA, CPA, and STC with HSA employing fluorescence spectroscopy, ultracentrifugation, ultrafiltration, and molecular modeling. Spectroscopic and ultracentrifugation studies demonstrated the formation of low-affinity BEA–HSA (K_a ≈ 10³ L/mol) and moderately strong CPA–HSA and STC–HSA complexes (K_a ≈ 10⁴ L/mol). In ultrafiltration experiments, CPA slightly displaced each site marker (warfarin, naproxen, and camptothecin) tested, while BEA and STC did not affect significantly the albumin binding of these drugs. Modeling studies suggest that CPA occupies Sudlow’s site I, while STC binds to the Heme site (FA1) on HSA. Considering the interactions of CPA with the site markers, the CPA–HSA interaction may have toxicological importance.

Molecular Conformer Search with Low-Energy Latent Space

Identifying low-energy conformers with quantum mechanical accuracy for molecules with many degrees of freedom is challenging. In this work, we use the molecular dihedral angles as features and explore the possibility of performing molecular conformer search in a latent space with a generative model named variational auto-encoder (VAE). We bias the VAE towards low-energy molecular configurations to generate more informative data. In this way, we can effectively build a reliable energy model for the low-energy potential energy surface. After the energy model has been built, we extract local-minimum conformations and refine them with structure optimization. We have tested and benchmarked our low-energy latent-space (LOLS) structure search method on organic molecules with 5–9 searching dimensions. Our results agree with previous studies.

GEOM, energy-annotated molecular conformations for property prediction and molecular generation

Machine learning (ML) outperforms traditional approaches in many molecular design tasks. ML models usually predict molecular properties from a 2D chemical graph or a single 3D structure, but neither of these representations accounts for the ensemble of 3D conformers that are accessible to a molecule. Property prediction could be improved by using conformer ensembles as input, but there is no large-scale dataset that contains graphs annotated with accurate conformers and experimental data. Here we use advanced sampling and semi-empirical density functional theory (DFT) to generate 37 million molecular conformations for over 450,000 molecules. The Geometric Ensemble Of Molecules (GEOM) dataset contains conformers for 133,000 species from QM9, and 317,000 species with experimental data related to biophysics, physiology, and physical chemistry. Ensembles of 1,511 species with BACE-1 inhibition data are also labeled with high-quality DFT free energies in an implicit water solvent, and 534 ensembles are further optimized with DFT. GEOM will assist in the development of models that predict properties from conformer ensembles, and generative models that sample 3D conformations.

Measurement(s)	Conformer geometries and properties
Technology Type(s)	Computational Chemistry

Incorporating NOE-Derived Distances in Conformer Generation of Cyclic Peptides with Distance Geometry

Nuclear magnetic resonance (NMR) data from NOESY (nuclear Overhauser enhancement spectroscopy) and ROESY (rotating frame Overhauser enhancement spectroscopy) experiments can easily be combined with distance geometry (DG) based conformer generators by modifying the molecular distance bounds matrix. In this work, we extend the modern DG based conformer generator ETKDG, which has been shown to reproduce experimental crystal structures from small molecules to large macrocycles well, to include NOE-derived interproton distances. In noeETKDG, the experimentally derived interproton distances are incorporated into the distance bounds matrix as loose upper (or lower) bounds to generate large conformer sets. Various subselection techniques can subsequently be applied to yield a conformer bundle that best reproduces the NOE data. The approach is benchmarked using a set of 24 (mostly) cyclic peptides for which NOE-derived distances as well as reference solution structures obtained by other software are available. With respect to other packages currently available, the advantages of noeETKDG are its speed and that no prior force-field parametrization is required, which is especially useful for peptides with unnatural amino acids. The resulting conformer bundles can be further processed with the use of structural refinement techniques to improve the modeling of the intramolecular nonbonded interactions. The noeETKDG code is released as a fully open-source software package available at www.github.com/rinikerlab/customETKDG.

Bifunctional Inhibitors of Influenza Virus Neuraminidase: Molecular Design of a Sulfonamide Linker

The growing resistance of the influenza virus to widely used competitive neuraminidase inhibitors occupying the active site of the enzyme requires the development of bifunctional compounds that can simultaneously interact with other regulatory sites on the protein surface. When developing such an inhibitor and combining structural fragments that could be located in the sialic acid cavity of the active site and the adjacent 430-cavity, it is necessary to select a suitable linker not only for connecting the fragments, but also to ensure effective interactions with the unique arginine triad Arg118-Arg292-Arg371 of neuraminidase. Using molecular modeling, we have demonstrated the usefulness of the sulfonamide group in the linker design and the potential advantage of this functional group over other isosteric analogues.

DockStream: a docking wrapper to enhance de novo molecular design

Recently, we have released the de novo design platform REINVENT in version 2.0. This improved and extended iteration supports far more features and scoring function components, which allows bespoke and tailor-made protocols to maximize impact in small molecule drug discovery projects. A major obstacle of generative models is producing active compounds, in which predictive (QSAR) models have been applied to enrich target activity. However, QSAR models are inherently limited by their applicability domains. To overcome these limitations, we introduce a structure-based scoring component for REINVENT. DockStream is a flexible, stand-alone molecular docking wrapper that provides access to a collection of ligand embedders and docking backends. Using the benchmarking and analysis workflow provided in DockStream, execution and subsequent analysis of a variety of docking configurations can be automated. Docking algorithms vary greatly in performance depending on the target and the benchmarking and analysis workflow provides a streamlined solution to identifying productive docking configurations. We show that an informative docking configuration can inform the REINVENT agent to optimize towards improving docking scores using public data. With docking activated, REINVENT is able to retain key interactions in the binding site, discard molecules which do not fit the binding cavity, harness unused (sub-)pockets, and improve overall performance in the scaffold-hopping scenario. The code is freely available at https://github.com/MolecularAI/DockStream .

Targeting the Central Pocket of the Pseudomonas aeruginosa Lectin LecA

Pseudomonas aeruginosa is an opportunistic ESKAPE pathogen that produces two lectins, LecA and LecB, as part of its large arsenal of virulence factors. Both carbohydrate‐binding proteins are central to the initial and later persistent infection processes, i. e. bacterial adhesion and biofilm formation. The biofilm matrix is a major resistance determinant and protects the bacteria against external threats such as the host immune system or antibiotic treatment. Therefore, the development of drugs against the P. aeruginosa biofilm is of particular interest to restore efficacy of antimicrobials. Carbohydrate‐based inhibitors for LecA and LecB were previously shown to efficiently reduce biofilm formations. Here, we report a new approach for inhibiting LecA with synthetic molecules bridging the established carbohydrate‐binding site and a central cavity located between two LecA protomers of the lectin tetramer. Inspired by in silico design, we synthesized various galactosidic LecA inhibitors with aromatic moieties targeting this central pocket. These compounds reached low micromolar affinities, validated in different biophysical assays. Finally, X‐ray diffraction analysis revealed the interactions of this compound class with LecA. This new mode of action paves the way to a novel route towards inhibition of P. aeruginosa biofilms.

The rise of deep learning and transformations in bioactivity prediction power of molecular modeling tools

The search and design for the better use of bioactive compounds are used in many experiments to best mimic compounds' functions in the human body. However, finding a cost-effective and timesaving approach is a top priority in different disciplines. Nowadays, artificial intelligence (AI) and particularly deep learning (DL) methods are widely applied to improve the precision and accuracy of models used in the drug discovery process. DL approaches have been used to provide more opportunities for a faster, efficient, cost-effective, and reliable computer-aided drug discovery. Moreover, the increasing biomedical data volume in areas, like genome sequences, medical images, protein structures, etc., has made data mining algorithms very important in finding novel compounds that could be drugs, uncovering or repurposing drugs and improving the area of genetic markers-based personalized medicine. Furthermore, deep neural networks (DNNs) have been demonstrated to outperform other techniques such as random forests and SVMs for QSAR studies and ligand-based virtual screening. Despite this, in QSAR studies, the quality of different data sources and potential experimental errors has greatly affected the accuracy of QSAR predictions. Therefore, further researches are still needed to improve the accuracy, selectivity, and sensitivity of the DL approach in building the best models of drug discovery.

Discovery of Novel Allosteric Modulators Targeting an Extra-Helical Binding Site of GLP-1R Using Structure- and Ligand-Based Virtual Screening

Allosteric modulators have emerged with many potential pharmacological advantages as they do not compete the binding of agonist or antagonist to the orthosteric sites but ultimately affect downstream signaling. To identify allosteric modulators targeting an extra-helical binding site of the glucagon-like peptide-1 receptor (GLP-1R) within the membrane environment, the following two computational approaches were applied: structure-based virtual screening with consideration of lipid contacts and ligand-based virtual screening with the maintenance of specific allosteric pocket residue interactions. Verified by radiolabeled ligand binding and cAMP accumulation experiments, two negative allosteric modulators and seven positive allosteric modulators were discovered using structure-based and ligand-based virtual screening methods, respectively. The computational approach presented here could possibly be used to discover allosteric modulators of other G protein-coupled receptors.

Synthesis, Antimicrobial, Antioxidant and Molecular Docking Studies on Novel 6-Methoxybenzothiazole-piperazine Derivatives with Propanamide Chain

BACKGROUND

Infectious diseases are a major threat in the developing world and the discovery of novel antimicrobial agents remains to be crucial due to acquired resistance by the microorganisms. Additionally, various diseases can be prevented with antioxidant agents as they can eliminate the harmful effects of reactive oxygen species.

OBJECTIVE

In this study, it was aimed to synthesize novel compounds bearing N-(6- methoxybenzothiazol-2-yl)-3-(4-substitued piperazinyl)propanamide backbone that had antimicrobial and antioxidant activities. Mechanisms of activity were aimed to be revealed by docking studies.

METHODS

Antimicrobial activities were tested by agar-based disc diffusion assay, and antioxidant activities were determined by CUPRAC assay.

RESULTS

In agar-based disc diffusion assay, the most active compounds were 2b and 2e against Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli and Candida albicans. Compounds 2e and 2j showed promising antioxidant activity in CUPRAC assay. Docking studies were performed to optimize the interactions of compounds with DNA gyrase subunit B of S. aureus. Under the light of docking studies, a new compound with potential GyrB inhibition was designed. Antioxidant activity was also supported by docking studies on superoxide dismutase 1 enzyme in which interactions with key residues were observed.

CONCLUSION

Ten novel benzothiazole-piperazine derivatives were synthesized and their antimicrobial and antioxidant activities were evaluated. Superoxide dismutase 1 enzyme was suggested to be a possible target for the antioxidant activity of the series.

A Novel Automated Screening Method for Combinatorially Generated Small Molecules

A main challenge in the enumeration of small-molecule chemical spaces for drug design is to quickly and accurately differentiate between possible and impossible molecules. Current approaches for screening enumerated molecules (e.g., 2D heuristics and 3D force fields) have not been able to achieve a balance between accuracy and speed. We have developed a new automated approach for fast and high-quality screening of small molecules, with the following steps: (1) for each molecule in the set, an ensemble of 2D descriptors as feature encoding is computed; (2) on a random small subset, classification (feasible/infeasible) targets via a 3D-based approach are generated; (3) a classification dataset with the computed features and targets is formed and a machine learning model for predicting the 3D approach's decisions is trained; and (4) the trained model is used to screen the remainder of the enumerated set. Our approach is ≈8× (7.96× to 8.84×) faster than screening via 3D simulations without significantly sacrificing accuracy; while compared to 2D-based pruning rules, this approach is more accurate, with better coverage of known feasible molecules. Once the topological features and 3D conformer evaluation methods are established, the process can be fully automated, without any additional chemistry expertise.

Efficient Amino Acid Conformer Search with Bayesian Optimization

Finding low-energy molecular conformers is challenging due to the high dimensionality of the search space and the computational cost of accurate quantum chemical methods for determining conformer structures and energies. Here, we combine active-learning Bayesian optimization (BO) algorithms with quantum chemistry methods to address this challenge. Using cysteine as an example, we show that our procedure is both efficient and accurate. After only 1000 single-point calculations and approximately 80 structure relaxations, which is less than 10% computational cost of the current fastest method, we have found the low-energy conformers in good agreement with experimental measurements and reference calculations. To test the transferability of our method, we also repeated the conformer search of serine, tryptophan, and aspartic acid. The results agree well with previous conformer search studies.

Covalent inhibitor reactivity prediction by the electrophilicity index-in and out of scope

Drug discovery is an expensive and time-consuming process. To make this process more efficient quantum chemistry methods can be employed. The electrophilicity index is one property that can be calculated by quantum chemistry methods, and if calculated correctly gives insight into the reactivity of covalent inhibitors. Herein we present the usage of the electrophilicity index on three common warheads, i.e., acrylamides, 2-chloroacetamides, and propargylamides. We thoroughly examine the properties of the electrophilicity index, show which pitfalls should be avoided, and what the requirements to successfully apply the electrophilicity index are.

ReSCoSS: a flexible quantum chemistry workflow identifying relevant solution conformers of drug-like molecules

Conformational equilibria are at the heart of drug design, yet their energetic description is often hampered by the insufficient accuracy of low-cost methods. Here we present a flexible and semi-automatic workflow based on quantum chemistry, ReSCoSS, designed to identify relevant conformers and predict their equilibria across different solvent environments in the Conductor-like Screening Model for Real Solvents (COSMO-RS) framework. We demonstrate the utility and accuracy of the workflow through conformational case studies on several drug-like molecules from literature where relevant conformations are known. We further show that including ReSCoSS conformers significantly improves COSMO-RS based predictions of physicochemical properties over single-conformation approaches. ReSCoSS has found broad adoption in the in-house drug discovery and development work streams and has contributed to establishing quantum-chemistry methods as a strategic pillar in ligand discovery.

Molecular simulation strategies for the discovery of selective inhibitors of β-catenin

Tumor dissemination and relapse in lung cancer were found to be due to the existence of cancer stem cells. In particular, the [Formula: see text]-catenin pathway is found to be one of the crucial pathways in maintaining the stem-like properties of the cells. Thus, targeting the [Formula: see text]-catenin family of proteins is a significant therapeutic route in the treatment of lung cancer. Therefore, in the present study, a pharmacophore-based drug repurposing approach was accomplished to pinpoint potent [Formula: see text]-catenin inhibitors from the DrugBank database. Primarily, ligand-based pharmacophore hypothesis (AAHHR) was generated using existing [Formula: see text]-catenin inhibitors available in the literature and utilized for library screening. Subsequently, the inhibitory activity of the screened compounds was examined by the hierarchical docking process and the Prime MM-GBSA algorithm. Moreover, quantum chemical calculations and molecular dynamics simulations were executed to analyze the inhibitory effects of the screened hit molecule. The results indicate that hit molecule, DB08047 was found to possess better binding free energy, favorable ligand strain energy, satisfactory pharmacokinetic properties and superior free energy landscape profile. Eventually, the pIC[Formula: see text] values of the lead compounds were predicted by the AutoQSAR algorithm. It is noteworthy to mention that DB08047 was found to possess pyrazole scaffolds which could downregulate [Formula: see text]-catenin pathway and thus facilitate the controlled cell growth/inhibit tumor growth.

BIreactive: A Machine-Learning Model to Estimate Covalent Warhead Reactivity

In the past decade, the pharmaceutical industry has paid closer attention to covalent drugs. Differently from standard noncovalent drugs, these compounds can exhibit peculiar properties, such as higher potency or longer duration of target inhibition with a potentially lower dosage. These properties are mainly driven by the reactive functional group present in the compound, the so-called warhead that forms a covalent bond with a specific nucleophilic amino-acid on the target. In this work, we report the possibility to combine ab initio activation energies with machine-learning to estimate covalent compound intrinsic reactivity. The idea behind this approach is to have a precise estimation of the transition state barriers, and thus of the compound reactivity, but with the speed of a machine-learning algorithm. We call this method "BIreactive". Here, we demonstrate this approach on acrylamides and 2-chloroacetamides, two warhead classes that possess different reaction mechanisms. In combination with our recently implemented truncation algorithm, we also demonstrate the possibility to use BIreactive not only for fragments but also for lead-like molecules. The generic nature of this approach allows also the extension to several other warheads. The combination of these factors makes BIreactive a valuable tool for the covalent drug discovery process in a pharmaceutical context.

Comparative Molecular Dynamics Simulations Provide Insight Into Antibiotic Interactions: A Case Study Using the Enzyme L,L-Diaminopimelate Aminotransferase (DapL)

The L,L-diaminopimelate aminotransferase (DapL) pathway, a recently discovered variant of the lysine biosynthetic pathway, is an attractive pipeline to identify targets for the development of novel antibiotic compounds. DapL is a homodimer that catalyzes the conversion of tetrahydrodipicolinate to L,L-diaminopimelate in a single transamination reaction. The penultimate and ultimate products of the lysine biosynthesis pathway, meso-diaminopimelate and lysine, are key components of the Gram-negative and Gram-positive bacterial peptidoglycan cell wall. Humans are not able to synthesize lysine, and DapL has been identified in 13% of bacteria whose genomes have been sequenced and annotated to date, thus it is an attractive target for the development of narrow spectrum antibiotics through the prevention of both lysine biosynthesis and peptidoglycan crosslinking. To address the common lack of structural information when conducting compound screening experiments and provide support for the use of modeled structures, our analyses utilized inferred structures from related homologous enzymes. Using a comprehensive and comparative molecular dynamics (MD) software package-DROIDS (Detecting Relative Outlier Impacts in Dynamic Simulations) 2.0, we investigated the binding dynamics of four previously identified antagonistic ligands of DapL from Verrucomicrobium spinosum, a non-pathogenic relative of Chlamydia trachomatis. Here, we present putative docking positions of the four ligands and provide confirmatory comparative molecular dynamics simulations supporting the conformations. The simulations performed in this study can be applied to evaluate putative targets to predict compound effectiveness prior to in vivo and in vitro experimentation. Moreover, this approach has the potential to streamline the process of antibiotic development.

Improving Conformer Generation for Small Rings and Macrocycles Based on Distance Geometry and Experimental Torsional-Angle Preferences

The conformer generator ETKDG is a stochastic search method that utilizes distance geometry together with knowledge derived from experimental crystal structures. It has been shown to generate good conformers for acyclic, flexible molecules. This work builds on ETKDG to improve conformer generation of molecules containing small or large aliphatic (i.e., non-aromatic) rings. For one, we devise additional torsional-angle potentials to describe small aliphatic rings and adapt the previously developed potentials for acyclic bonds to facilitate the sampling of macrocycles. However, due to the larger number of degrees of freedom of macrocycles, the conformational space to sample is much broader than for small molecules, creating a challenge for conformer generators. We therefore introduce different heuristics to restrict the search space of macrocycles and bias the sampling toward more experimentally relevant structures. Specifically, we show the usage of elliptical geometry and customizable Coulombic interactions as heuristics. The performance of the improved ETKDG is demonstrated on test sets of diverse macrocycles and cyclic peptides. The code developed here will be incorporated into the 2020.03 release of the open-source cheminformatics library RDKit.

Homology modeling, structural insights and in-silico screening for selective inhibitors of mycobacterial CysE

Tuberculosis posses a major threat for health practitioners due to lengthy treatment regimen, increase in the drug-resistant strains of Mycobacterium tuberculosis (M. tb) and unavailability of drugs for its persistent form. Therefore, there is an urgent need for discovery of new and improved anti-tubercular drugs. In M. tb, the two step de novo biosynthesis of L-cysteine, an essential metabolic pathway is reported to be up-regulated in the persistent phase of the organism, involves two enzymes CysE and CysK. Although, structural insights for rational drug discovery are available for the later, not much information is known for the former. This study proposes a 3-dimensional model of M. tb CysE followed by in-silico screening of 67,030 anti-tuberculosis bioactive compounds. Subsequently, post-processing of 1000 best hits was carried out and top 200 compounds thus obtained were docked into the active site cleft of E. coli homologue as a control, but revealed unexpected results. Differences in the active site architectures and comparative analysis of molecular electrostatic potentials between the two CysEs provide molecular basis for the compounds C1, C3, C4 and C7 exhibiting preferential binding for M. tb CysE. In addition, shorter N-terminus along with positive and irregular trimeric base of M. tb CysE indicates its biological assembly as trimer. Based on mapping of residues involved in cysteine sensitivity on to the model structure of M. tb CysE, it is hypothesized that feedback inhibition of this homologue by cysteine may be affected.Communicated by Ramaswamy H. Sarma.

Identification of potential inhibitors against pathogenic missense mutations of PMM2 using a structure-based virtual screening approach

The autosomal recessive phosphomannomutase 2-congenital disorder of glycosylation (PMM2-CDG) is characterized by defective functioning of the PMM2 enzyme, which is necessary for the conversion of mannose-6-phosphate into mannose-1-phosphate. Here, a computational pipeline was drawn to identify the most significant mutations, and further, we used a virtual screening approach to identify a new lead compound to treat the identified significant mutations. We searched for missense mutation data related to PMM2-CDG in HGMD®, UniProt, and ClinVar. Our search yielded a total of 103 mutations, of which 91 are missense mutations. The D65Y, I132N, I132T, and F183S mutations were classified as deleterious, destabilizing, and altering the biophysical properties using the PredictSNP, iStable, and Align GVGD in silico prediction tools. Additionally, we applied a multistep protocol to screen for an alternative lead compound to the existing CID2876053 (1-(3-chlorophenyl)-3,3-bis(pyridine-2-yl)urea) with affinity to these identified significant mutants. Two compounds, CHEMBL1491007 (6-chloro-4-phenyl-3-(4-pyridin-2-ylpiperazin-1-yl)-1H-quinolin-2-one) and CHEMBL3653029 (5-chloro-4-[6-[(3-fluorophenyl)methylamino]pyridin-2-yl]-N-(piperidin-4-ylmethyl)pyridin-2-amine), exhibited the highest binding affinity with the selected mutants and were chosen for further analysis. Through molecular docking, molecular dynamics simulation, and MMPBSA analysis, we found that the known compound, i.e. CID2876053, has stronger interaction with the D65Y mutant. The newly identified lead compound CHEMBL1491007 showed stronger interaction with the I132N and I132T mutants, whereas the most deleterious mutant, F183S, showed stronger interaction with CHEMBL3653029. This study is expected to aid in the field of precision medicine, and further to in vivo and in vitro analysis of these lead compounds might shed light on the treatment of PMM2-CDG. Communicated by Ramaswamy H. Sarma.

Molecular Geometry Prediction using a Deep Generative Graph Neural Network

A molecule's geometry, also known as conformation, is one of a molecule's most important properties, determining the reactions it participates in, the bonds it forms, and the interactions it has with other molecules. Conventional conformation generation methods minimize hand-designed molecular force field energy functions that are often not well correlated with the true energy function of a molecule observed in nature. They generate geometrically diverse sets of conformations, some of which are very similar to the lowest-energy conformations and others of which are very different. In this paper, we propose a conditional deep generative graph neural network that learns an energy function by directly learning to generate molecular conformations that are energetically favorable and more likely to be observed experimentally in data-driven manner. On three large-scale datasets containing small molecules, we show that our method generates a set of conformations that on average is far more likely to be close to the corresponding reference conformations than are those obtained from conventional force field methods. Our method maintains geometrical diversity by generating conformations that are not too similar to each other, and is also computationally faster. We also show that our method can be used to provide initial coordinates for conventional force field methods. On one of the evaluated datasets we show that this combination allows us to combine the best of both methods, yielding generated conformations that are on average close to reference conformations with some very similar to reference conformations.

Development of QSAAR and QAAR models for predicting fish early-life stage toxicity with a focus on industrial chemicals

We developed models for predicting fish early-life stage (ELS) toxicities oriented to industrial chemicals. The training set was constructed without data from the Office of Pesticide Programs Pesticide Ecotoxicity Database, the main source for the pesticide-biased training set used in our previous work (SAR QSAR Environ. Res. 29:9, 725-742). In addition to the descriptors from the previous study, we also used water solubility to develop the new models, which were evaluated against the test set used in our previous study so that we could focus on the effects of the different training set and the additional descriptor. The statistics for the new models were hardly better than those for the previous models, which suggests, contrary to our expectations, that pesticide-biased data can successfully be used to develop models for predicting the fish ELS toxicities oriented to industrial chemicals. Acute Daphnia magna toxicity was important for the predictive QSAARs in both studies. A distance-based method for defining the applicability domains indicated that water solubility was a key indicator for detecting underestimated chemicals. The comparison of fish ELS toxicities for chemicals presented in different literatures revealed the uncertainty of the experimental data, which may lead to the low predictivity.

A Fast Ab Initio Predictor Tool for Covalent Reactivity Estimation of Acrylamides

Thanks to their unique mode of action, covalent drugs represent an exceptional opportunity for drug design. After binding to a biologically relevant target system, covalent compounds form a reversible or irreversible covalent bond with a nucleophilic amino acid. Due to the inherently large binding energy of a covalent bond, covalent binders exhibit higher potencies and thus allow potentially lower drug dosages. However, a proper balancing of compound reactivity is key for the design of covalent binders, to achieve high levels of target inhibition while minimizing promiscuous covalent binding to nontarget proteins. In this work, we demonstrated the possibility to apply the electrophilicity index concept to estimate covalent compound reactivity. We tested this approach on acrylamides, one of the most prominent classes of covalent warheads. Our study clearly demonstrated that, for compounds with molecular weight (MW) below 250 Da, the electrophilicity index can be directly used to estimate compound reactivity. On the other hand, for leadlike molecules (MW > 250 Da) we implemented a new truncation algorithm that has to be applied before reactivity calculations. This algorithm can ensure the localization of HOMO/LUMO orbitals on the compound warhead and thus a correct estimation of its reactivity. Our results also indicate that caution should be used when employing the electrophilicity index to estimate the reactivity of nonterminal acrylamides. The nonparametric nature of this method and its reasonable computational cost make it a suitable tool to support covalent drug design.

How to Computationally Stack the Deck for Hit-to-Lead Generation: In Silico Molecular Interaction Energy Profiling for de Novo siRNA Guide Strand Surrogate Selection

The Argonaute-2 protein is part of the RNA-induced silencing complex (RISC) and anchors the guide strand of the small interfering RNA (siRNA). The 3'-end of the RNA contains two unpaired nucleotides (3'-overhang) that interact with the PAZ (PIWI/Argonaute/Zwille) domain of the protein. Theoretical and experimental evidence points toward a direct connection between the PAZ/3'-overhang binding affinity and siRNA's potency and specificity. Among the challenges to overcome when deploying siRNA molecules as therapeutics are their ready degradation under physiological conditions and off-target effects. It has been demonstrated that nuclease resistance can be improved via replacement of the dinucleotide overhang by small molecules which retain the interactions of the RNA guide strand with the PAZ domain. Most commonly, nucleotide analogues are used to substitute the siRNA overhang. However, in this study we adopt a de novo approach to its modification. The X-ray structure of human Argonaute-2 PAZ domain served to perform virtual screening and molecular interaction energy profiling (i.e., decomposition of the force field calculated protein-ligand interaction energies) of tailored-to-purpose fragment libraries. The binding of fragments to the PAZ domain was validated experimentally by NMR spectroscopy. The in silico guided protocol led to the efficient discovery of a number of PAZ domain ligands with affinities comparable to that of a reference dinucleotide (UpU, K_d = 33 μM). Originally starting from a generic fragment library, hits progress from 930 μM down to 14 μM within three iterations for the fragments selected via in silico molecular interaction energy profiling from a bespoke library. These dinucleotide siRNA guide strand surrogates represent potential new siRNA-based therapeutics (when attached to siRNA to form bioconjugates) featuring improved efficacy, specificity, stability, and cellular uptake. This project yielded a portfolio of seven patent applications, four of which have been granted to date.

Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure-Activity Relationship (QSAR) Analysis

Numerous chemical compounds are distributed around the world and may affect the homeostasis of the endocrine system by disrupting the normal functions of hormone receptors. Although the risks associated with these compounds have been evaluated by acute toxicity testing in mammalian models, the chronic toxicity of many chemicals remains due to high cost of the compounds and the testing, etc. However, computational approaches may be promising alternatives and reduce these evaluations. Recently, deep learning (DL) has been shown to be promising prediction models with high accuracy for recognition of images, speech, signals, and videos since it greatly benefits from large datasets. Recently, a novel DL-based technique called DeepSnap was developed to conduct QSAR analysis using three-dimensional images of chemical structures. It can be used to predict the potential toxicity of many different chemicals to various receptors without extraction of descriptors. DeepSnap has been shown to have a very high capacity in tests using Tox21 quantitative qHTP datasets. Numerous parameters must be adjusted to use the DeepSnap method but they have not been optimized. In this study, the effects of these parameters on the performance of the DL prediction model were evaluated in terms of the loss in validation as an indicator for evaluating the performance of the DL using the toxicity information in the Tox21 qHTP database. The relations of the parameters of DeepSnap such as (1) number of molecules per SDF split into (2) zoom factor percentage, (3) atom size for van der waals percentage, (4) bond radius, (5) minimum bond distance, and (6) bond tolerance, with the validation loss following quadratic function curves, which suggests that optimal thresholds exist to attain the best performance with these prediction models. Using the parameter values set with the best performance, the prediction model of chemical compounds for CAR agonist was built using 64 images, at 105° angle, with AUC of 0.791. Thus, based on these parameters, the proposed DeepSnap-DL approach will be highly reliable and beneficial to establish models to assess the risk associated with various chemicals.

Energy windows for computed compound conformers: covering artefacts or truly large reorganization energies?

The generation of 3D conformers of small molecules underpins most computational drug discovery. Thus, the conformer quality is critical and depends on their energetics. A key parameter is the empirical conformational energy window (ΔEw), since only conformers within ΔEw are retained. However, ΔEw values in use appear unrealistically large. We analyze the factors pertaining to the conformer energetics and ΔEw. We argue that more attention must be focused on the problem of collapsed low-energy conformers. That is due to artificial intramolecular stabilization and occurs even with continuum solvation. Consequently, the conformational energy of extended bioactive structures is artefactually increased, which inflates ΔEw. Thus, this Perspective highlights the issues arising from low-energy conformers and suggests improvements via empirical or physics-based strategies.

Development of models to predict fish early-life stage toxicity from acute Daphnia magna toxicity$

Herein, we propose models for predicting fish early-life stage (ELS) toxicity from acute Daphnia magna toxicity and various molecular descriptors. Specifically, eight models were developed with fathead minnow (Pimephales promelas) data and were validated against Japanese medaka (Oryzias latipes) data because the quantity of available Japanese medaka data is much smaller than the quantity of fathead minnow data. The training data set for the models consisted of ELS fathead minnow toxicity data for 77 chemicals; data for 67 of the 77 chemicals originated from the OPP Pesticide Ecotoxicity Database of the US Environmental Protection Agency. The training data were biased toward pesticides. A simple quantitative activity-activity relationship (QAAR) model based on the correlation between fish ELS and acute Daphnia magna toxicities showed good predictivity for the chemicals in the external validation data set relative to the predictivities of the other models in this study. However, goodness-of-fit and robustness were better for quantitative structure-activity-activity relationship (QSAAR) models that included molecular descriptors (such as pesticide-related atoms and substructures as well as molecular weight and three-dimensional-structure-based parameters). A battery approach involving the use of both the QAAR and the QSAARs might enhance the reliability of the estimated values and prevent underestimates.