In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids.

Malaria is a parasitic tropical disease that kills around 600,000 patients every year. The emergence of resistant Plasmodium falciparum parasites to artemisinin-based combination therapies (ACTs) represents a significant public health threat, indicating the urgent need for new effective compounds to reverse ACT resistance and cure the disease. For this, extensive curation and homogenization of experimental anti-Plasmodium screening data from both in-house and ChEMBL sources were conducted. As a result, a coherent strategy was established that allowed compiling coherent training sets that associate compound structures to the respective antimalarial activity measurements. Seventeen of these training sets led to the successful generation of classification models discriminating whether a compound has a significant probability to be active under the specific conditions of the antimalarial test associated with each set. These models were used in consensus prediction of the most likely active from a series of curcuminoids available in-house. Positive predictions together with a few predicted as inactive were then submitted to experimental in vitro antimalarial testing. A large majority from predicted compounds showed antimalarial activity, but not those predicted as inactive, thus experimentally validating the in silico screening approach. The herein proposed consensus machine learning approach showed its potential to reduce the cost and duration of antimalarial drug discovery

Structural and Physico-Chemical Interpretation (SPCI) of QSAR Models and Its Comparison with Matched Molecular Pair Analysis

© 2016 American Chemical Society.This paper describes the Structural and Physico-Chemical Interpretation (SPCI) approach, which is an extension of a recently reported method for interpretation of quantitative structure-activity relationship (QSAR) models. This approach can efficiently be used to reveal structural motifs and the major physicochemical factors affecting the investigated properties. Its efficacy was demonstrated both on the classical Free-Wilson data set and on several data sets with different end points (permeability of the blood-brain barrier, fibrinogen receptor antagonists, acute oral toxicity). Structure-activity patterns extracted from QSAR models with SPCI were in good correspondence with experimentally observed relationships and molecular docking, regardless of the machine learning method used. Comparison of SPCI with the matched molecular pair (MMP) method clearly shows an advantage of our approach over MMP, especially for small or structurally diverse data sets. The developed approach has been implemented in the SPCI software tool with a graphical user interface, which is publicly available at http://qsar4u.com/pages/sirms-qsar.php

Synthesis, biological evaluation, X-ray molecular structure and molecular docking studies of RGD mimetics containing 6-amino-2,3-dihydroisoindolin-1-one fragment as ligands of integrin αIIbβ3

AbstractA series of novel RGD mimetics containing phthalimidine fragment was designed and synthesized. Their antiaggregative activity determined by Born’s method was shown to be due to inhibition of fibrinogen binding to αIIbβ3. Molecular docking of RGD mimetics to αIIbβ3 receptor showed the key interactions in this complex, and also some correlations have been observed between values of biological activity and docking scores. The single crystal X-ray data were obtained for five mimetics

On Predicting Mössbauer Parameters of Iron-Containing Molecules with Density-Functional Theory

The performance of six frequently used density functional theory (DFT) methods (RPBE, OLYP, TPSS, B3LYP, B3LYP*, and TPSSh) in the prediction of Mössbauer isomer shifts(δ) and quadrupole splittings (ΔEQ) is studied for an extended and diverse set of Fe complexes. In addition to the influence of the applied density functional and the type of the basis set, the effect of the environment of the molecule, approximated with the conducting-like screening solvation model (COSMO) on the computed Mössbauer parameters, is also investigated. For the isomer shifts the COSMO-B3LYP method is found to provide accurate δ values for all 66 investigated complexes, with a mean absolute error (MAE) of 0.05 mm s–1 and a maximum deviation of 0.12 mm s–1. Obtaining accurate ΔEQ values presents a bigger challenge; however, with the selection of an appropriate DFT method, a reasonable agreement can be achieved between experiment and theory. Identifying the various chemical classes of compounds that need different treatment allowed us to construct a recipe for ΔEQ calculations; the application of this approach yields a MAE of 0.12 mm s–1 (7% error) and a maximum deviation of 0.55 mm s–1 (17% error). This accuracy should be sufficient for most chemical problems that concern Fe complexes. Furthermore, the reliability of the DFT approach is verified by extending the investigation to chemically relevant case studies which include geometric isomerism, phase transitions induced by variations of the electronic structure (e.g., spin crossover and inversion of the orbital ground state), and the description of electronically degenerate triplet and quintet states. Finally, the immense and often unexploited potential of utilizing the sign of the ΔEQ in characterizing distortions or in identifying the appropriate electronic state at the assignment of the spectral lines is also shown

CATMoS: Collaborative Acute Toxicity Modeling Suite.

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495

Quantitative Structure–Property Relationship Studies on Ostwald Solubility and Partition Coefficients of Organic Solutes in Ionic Liquids

Article discussing quantitative structure-property relationship studies on Ostwald solubility and partition coefficients of organic solutes in ionic liquids

A community effort in SARS-CoV-2 drug discovery.

peer reviewedThe COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against Covid-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.R-AGR-3826 - COVID19-14715687-CovScreen (01/06/2020 - 31/01/2021) - GLAAB Enric

Structural and Physico-Chemical Interpretation (SPCI) of QSAR Models and Its Comparison with Matched Molecular Pair Analysis

© 2016 American Chemical Society.This paper describes the Structural and Physico-Chemical Interpretation (SPCI) approach, which is an extension of a recently reported method for interpretation of quantitative structure-activity relationship (QSAR) models. This approach can efficiently be used to reveal structural motifs and the major physicochemical factors affecting the investigated properties. Its efficacy was demonstrated both on the classical Free-Wilson data set and on several data sets with different end points (permeability of the blood-brain barrier, fibrinogen receptor antagonists, acute oral toxicity). Structure-activity patterns extracted from QSAR models with SPCI were in good correspondence with experimentally observed relationships and molecular docking, regardless of the machine learning method used. Comparison of SPCI with the matched molecular pair (MMP) method clearly shows an advantage of our approach over MMP, especially for small or structurally diverse data sets. The developed approach has been implemented in the SPCI software tool with a graphical user interface, which is publicly available at http://qsar4u.com/pages/sirms-qsar.php

Structural and Physico-Chemical Interpretation (SPCI) of QSAR Models and Its Comparison with Matched Molecular Pair Analysis

© 2016 American Chemical Society.This paper describes the Structural and Physico-Chemical Interpretation (SPCI) approach, which is an extension of a recently reported method for interpretation of quantitative structure-activity relationship (QSAR) models. This approach can efficiently be used to reveal structural motifs and the major physicochemical factors affecting the investigated properties. Its efficacy was demonstrated both on the classical Free-Wilson data set and on several data sets with different end points (permeability of the blood-brain barrier, fibrinogen receptor antagonists, acute oral toxicity). Structure-activity patterns extracted from QSAR models with SPCI were in good correspondence with experimentally observed relationships and molecular docking, regardless of the machine learning method used. Comparison of SPCI with the matched molecular pair (MMP) method clearly shows an advantage of our approach over MMP, especially for small or structurally diverse data sets. The developed approach has been implemented in the SPCI software tool with a graphical user interface, which is publicly available at http://qsar4u.com/pages/sirms-qsar.php