Calibration of DFT-based Models with Experimental Data

JAS thanks David Ponting and co‐workers at Lhasa Limited for useful suggestions and discussions. This work was also supported by the National Natural Science Foundation of China [Grant number 21875061, 21975066] and the program for Science & Technology Innovation Team in Universities of Henan Province [Grant number 19IRTSTHN029]. Publisher Copyright: © 2022 The Authors. Molecular Informatics published by Wiley-VCH GmbH.Random Forest (RF) QSPR models were developed with a data set of homolytic bond dissociation energies (BDE) previously calculated by B3LYP/6-311++G(d,p)//DFTB for 2263 sp3C−H covalent bonds. The best set of attributes consisted in 114 descriptors of the carbon atom (counts of atom types in 5 spheres around the kernel atom and ring descriptors). The optimized model predicted the DFT-calculated BDE of an independent test set of 224 bonds with MAE=2.86 kcal/mol. A new data set of 409 bonds from the iBonD database (http://ibond.nankai.edu.cn) was predicted by the RF with a modest MAE (5.36 kcal/mol) but a relatively high R2 (0.75) against experimental energies. A prediction scheme was explored that corrects the RF prediction with the average deviation observed for the k nearest neighbours (KNN) in an additional memory of experimental data. The corrected predictions achieved MAE=2.22 kcal/mol for an independent test set of 145 bonds and the corresponding experimental bond energies.publishersversionpublishe

Team-Based Learning in Chemistry Courses with Laboratory Sessions

[EN] The implementation of Team-Based Learning (TBL, http://www.teambasedlearning.org) in one-semester undergraduate courses of chemistry offered to first year students is reported. TBL is an active learning instructional strategy heavily relying on small group interaction. Teaching lab classes in a TBL context presented a specific challenge, as decisions were required about their role in the global framework and the possibility of incorporating lab activities as “teamwork”. The design of lab sessions as TBL team application activities is here also illustrated, both for a course of General Chemistry and a course of Organic Chemistry. TBL dramatically improved students class attendance and participation. Its implementation has provided a unique opportunity for the pedagogical development of teaching staff. A moderate number of students reported discomfort with TBL: the requirement of individual preparation before classes and the impact of team participation in the final grade is indeed a new ground for most students, often perceived as a troubling deviation from the common social paradigm of the learning process. The role of the instructor as a facilitator of individual and team work, and the clear explanation of the method are thus of utmost relevance.The authors thank Faculdade de Ciências e Tecnologia (Universidade Nova de Lisboa) for financial supportAires-De-Sousa, J.; Cardoso, MM.; Ferreira, L.; Lima, J.; Noronha, J.; Nunes, A.; Ponte, M. (2017). Team-Based Learning in Chemistry Courses with Laboratory Sessions. En Proceedings of the 3rd International Conference on Higher Education Advances. Editorial Universitat Politècnica de València. 1213-1218. https://doi.org/10.4995/HEAD17.2017.5559OCS1213121

QSAR modeling of antitubercular activity of diverse organic compounds

Tuberculosis (TB) is a worldwide infectious disease that has shown over time extremely high mortality levels. The urgent need to develop new antitubercular drugs is due to the increasing rate of appearance of multi-drug resistant strains to the commonly used drugs, and the longer durations of therapy and recovery, particularly in immuno-compromised patients. The major goal of the present study is the exploration of data from different families of compounds through the use of a variety of machine learning techniques so that robust QSAR-based models can be developed to further guide in the quest for new potent anti-TB compounds. Eight QSAR models were built using various types of descriptors (from ADRIANA.Code and Dragon software) with two publicly available structurally diverse data sets, including recent data deposited in PubChem. QSAR methodologies used Random Forests and Associative Neural Networks. Predictions for the external evaluation sets obtained accuracies in the range of 0.76-0.88 (for active/inactive classifications) and Q(2)=0.66-0.89 for regressions. Models developed in this study can be used to estimate the anti-TB activity of drug candidates at early stages of drug development (C) 2011 Elsevier B.V. All rights reserved

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu

Thrombocytopenia and platelet transfusions in ICU patients: an international inception cohort study (PLOT-ICU)

Purpose Thrombocytopenia (platelet count < 150 × 109/L) is common in intensive care unit (ICU) patients and is likely associated with worse outcomes. In this study we present international contemporary data on thrombocytopenia in ICU patients. Methods We conducted a prospective cohort study in adult ICU patients in 52 ICUs across 10 countries. We assessed frequencies of thrombocytopenia, use of platelet transfusions and clinical outcomes including mortality. We evaluated pre-selected potential risk factors for the development of thrombocytopenia during ICU stay and associations between thrombocytopenia at ICU admission and 90-day mortality using pre-specified logistic regression analyses. Results We analysed 1166 ICU patients; the median age was 63 years and 39.5% were female. Overall, 43.2% (95% confidence interval (CI) 40.4–46.1) had thrombocytopenia; 23.4% (20–26) had thrombocytopenia at ICU admission, and 19.8% (17.6–22.2) developed thrombocytopenia during their ICU stay. Non-AIDS-, non-cancer-related immune deficiency, liver failure, male sex, septic shock, and bleeding at ICU admission were associated with the development of thrombocytopenia during ICU stay. Among patients with thrombocytopenia, 22.6% received platelet transfusion(s), and 64.3% of in-ICU transfusions were prophylactic. Patients with thrombocytopenia had higher occurrences of bleeding and death, fewer days alive without the use of life-support, and fewer days alive and out of hospital. Thrombocytopenia at ICU admission was associated with 90-day mortality (adjusted odds ratio 1.7; 95% CI 1.19–2.42). Conclusion Thrombocytopenia occurred in 43% of critically ill patients and was associated with worse outcomes including increased mortality. Platelet transfusions were given to 23% of patients with thrombocytopenia and most were prophylactic.publishedVersio

Thrombocytopenia and platelet transfusions in ICU patients: an international inception cohort study (PLOT-ICU)

Purpose: Thrombocytopenia (platelet count &lt; 150 × 109/L) is common in intensive care unit (ICU) patients and is likely associated with worse outcomes. In this study we present international contemporary data on thrombocytopenia in ICU patients. Methods: We conducted a prospective cohort study in adult ICU patients in 52 ICUs across 10 countries. We assessed frequencies of thrombocytopenia, use of platelet transfusions and clinical outcomes including mortality. We evaluated pre-selected potential risk factors for the development of thrombocytopenia during ICU stay and associations between thrombocytopenia at ICU admission and 90-day mortality using pre-specified logistic regression analyses. Results: We analysed 1166 ICU patients; the median age was 63 years and 39.5% were female. Overall, 43.2% (95% confidence interval (CI) 40.4–46.1) had thrombocytopenia; 23.4% (20–26) had thrombocytopenia at ICU admission, and 19.8% (17.6–22.2) developed thrombocytopenia during their ICU stay. Absence of acquired immune deficiency syndrome (AIDS), non-cancer-related immune deficiency, liver failure, male sex, septic shock, and bleeding at ICU admission were associated with the development of thrombocytopenia during ICU stay. Among patients with thrombocytopenia, 22.6% received platelet transfusion(s), and 64.3% of in-ICU transfusions were prophylactic. Patients with thrombocytopenia had higher occurrences of bleeding and death, fewer days alive without the use of life-support, and fewer days alive and out of hospital. Thrombocytopenia at ICU admission was associated with 90-day mortality (adjusted odds ratio 1.7; 95% CI 1.19–2.42). Conclusion: Thrombocytopenia occurred in 43% of critically ill patients and was associated with worse outcomes including increased mortality. Platelet transfusions were given to 23% of patients with thrombocytopenia and most were prophylactic

GUIDEMOL: a Python graphical user interface for molecular descriptors based on RDKit

GUIDEMOL is a Python computer program based on the RDKit software to process molecular structures and calculate molecular descriptors with a graphical user interface using the tkinter package. It can calculate descriptors already implemented in RDKit as well as grid representations of 3D molecular structures using the electrostatic potential or voxels. The GUIDEMOL app provides an easy access to RDKit tools for chemoinformatics users with no programming skills and can be adapted to calculate other descriptors or to trigger other procedures. A CLI is also provided for the calculation of grid representations. The source code is available at https://github.com/jairesdesousa/guidemo

Computational Methodologies in the Exploration of Marine Natural Product Leads

Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure&ndash;Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review

Atom Condensed Fukui Functions Calculated for 2973 Organic Molecules

<p>Data set Fukui_2973</p> <p>==================</p> <p>Atomic NBO charges for non-hydrogen atoms in 2973 small organic molecules at the B3LYP/6-31G*//DFTB level of theory.</p> <p> </p> <p> </p> <p>Related publication:</p> <p>* Qingyou Zhang, Fangfang Zheng, Tanfeng Zhao, Xiaohui Qu, João Aires-de-Sousa:</p> <p>Machine Learning Estimation of Atom Condensed Fukui Functions.</p> <p>Molecular Informatics (2015)</p> <p>DOI: 10.1002/minf.201500113</p> <p> </p> <p>This data set is publicly available at</p> <p>http://dx.doi.org/10.6084/m9.figshare.1400514</p> <p> </p> <p>Files</p> <p>-----</p> <p>Fukui_2973_sdf.tar.gz - 2973 molecules in the MDL SDFile format</p> <p>charges_Fukui_2973.xlsx - NBO atomic charges for the non-hydrogen atoms in neutral and charged species</p> <p> </p> <p>Molecules</p> <p>---------</p> <p>For a subset of the fragment-like ZINC database [1] consisting of 2973 neutral organic molecules composed from elements H,C,N,O,S, molecular geometries were relaxed by DFTB+ [2] and atomic charges were calculated by the NBO 5.9 program [3] from a natural population analysis on the B3LYP/6-31G* wavefunction. Charged species (+1:cation and -1:anion) were calculated with the geometry obtained for the corresponding neutral species.</p> <p> </p> <p>Format</p> <p>------</p> <p>Each molecule is stored in its own file, ending in ".sdf".</p> <p>The format is the standard MDL SDFile generated with the Marvin/JChem 5.8.2, 2012, software [5].</p> <p>Atomic charges are stored in the charges_Fukui_2973.xlsx file. Three different sheets are used for the neutral, cation and anion species respectively.</p> <p> </p> <p>Column Content</p> <p>------ -------</p> <p>1 Molecule ID (as appears in the corresponding .sdf file name and in the ZINC database)</p> <p>2,... Atomic charge (in elementary charge units) for atoms in the same sequence as in the corresponding .sdf file</p> <p> </p> <p>References</p> <p>----------</p> <p>[1] Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG: ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 2012, 52: 1757-1768.</p> <p>[2] Aradi B, Hourahine B, Frauenheim T: DFTB+, a sparse matrix-based implementation of the DFTB method. J Phys Chem A 2007, 111:2678-5684.</p> <p>[3] NBO 5.9. Glendening ED, Badenhoop JK, Reed AE, JCarpenter JE, Bohmann JA, Morales CM, Weinhold F: Theoretical Chemistry Institute, University of Wisconsin, Madison, WI, 2011; [http://www.chem.wisc.edu/~nbo5].</p> <p>[4] Schmidt MW, Baldridge KK, Boatz JA, Elbert ST, Gordon MS, Jensen JJ, Koseki S, Matsunaga N, Nguyen KA, Su S, Windus TL, Dupuis M, Montgomery JA: General atomic and molecular electronic structure system. J Comput Chem 1993, 14:1347-1363. GAMESS Version 11 Aug 2011 (R1)</p> <p>[5] ChemAxon [http://www.chemaxon.com/]</p

Energies of the HOMO and LUMO Orbitals for 111725 Organic Molecules Calculated by DFT B3LYP / 6-31G*

HOMO and LUMO orbital energies for 111725 organic molecules calculated at the B3LYP/6-31G*//PM6 or B3LYP/6-31G*//PM7 level of theory.<br><br>Related publication:<br><br>* Florbela Pereira, Kaixia Xiao, Diogo A. R. S. Latino, Chengcheng Wu, Qingyou Zhang and Joao Aires-de-Sousa:<br><br>Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals.<br><br>J. Chem. Inf. Model. (2017)<br><br>DOI: <a href="http://dx.doi.org/10.1021/acs.jcim.6b00340">10.1021/acs.jcim.6b00340</a><br> <br><br>This data set is publicly available at http://dx.doi.org/10.6084/m9.figshare.3384184.v1<br><br> <br><br>Files<br>-----<br><br>frontier_orbitals_111725mols_sdf.tar.gz - 111275 molecules in the MDL SDFile format<br><br>frontier_orbitals_111725mols.xlsx - HOMO and LUMO orbital energies for 111275 neutral organic molecules<br><br>coordinates_111725mols_xyz.zip - atomic coordinates used for the DFT calculation of the 111275 molecules<br><br>PM7_frontier_orbitals.xlsx - HOMO and LUMO energies calculated by the PM7 semi-empirical method.<br><br><br><br>Molecules<br>---------<br><br>For the database creation, molecular structural motifs were retrieved from organic electronics studies, and collections of dyes, metabolites and electrophiles/nucleophiles [1-5]. The database was populated by retrieval of similar examples from the ZINC database [6], the PubChem database [7] and by computationally combining motifs and lists of substituents with the ChemAxon Reactor software, JChem 15.4.6, 2015, ChemAxon (http://www.chemaxon.com). The structures were standardized with ChemAxon Standardizer (JChem 15.4.6, 2015, ChemAxon, http://www.chemaxon.com) and OpenBabel (Open Babel Package, version 2.3.1 http://openbabel.org) for neutralization and inclusion of all hydrogen atoms. The molecular structures include atomic elements C, H, B, N, O, F, Si, P, S, Cl, Se, and Br.<br><br>Molecular geometries were relaxed by the PM6 or PM7 methods using the MOPAC software [8] and orbital energies were calculated by the GAMESS program [9] with the B3LYP functional and the 6-31G* basis set. Structures were calculated with the geometry obtained with the PM6 or PM7 semi-empirical method.<br><br> <br>Format<br>------<br><br>Each molecule is stored in its own file, ending in ".sdf". These are the starting structures, previous to geometry relaxation with the MOPAC program. <br><br>The format is the standard MDL SDFile generated with ChemAxon Standardizer and OpenBabel.<br><br>The atomic coordinates obtained with the PM6 and PM7 methods are stored in files ending in ".xyz", one for each molecule. Each file comprises a header line specifying the number of atoms <i>n</i>, a line with the id of the structure, and <i>n</i> lines containing the element and atomic coordinates, one atom per line.<br><br>Orbital energies are stored in the frontier_orbitals_111725mols.xlsx file. Two different sheets are used for the main database and a data set used as final test set in the related publication. PM7 values are stored in the PM7_frontier_orbitals.xlsx with the same format. <br><br><br>Column Content of .xlsx files<br>------<br><br>1 Molecule ID (as appears in the corresponding .sdf file name)<br><br>2 HOMO energy in eV.<br><br>3 LUMO energy in eV.<br><br><br>References<br>----------<br><br>[1] Po R, Bianchi G, Carbonera C, Pellegrino A: All that glisters is not gold: an analysis of the synthetic complexity of efficient polymer donors for polymer solar cells. Macromolecules 2015, 48:453-461.<br><br>[2] Hachmann J, Olivares-Amaya R, Atahan-Evrenk S, Amador-Bedolla C, Sanchez-Carrera RS, Gold-Parker A, Vogt L, Brockway AM, Aspuru-Guzik A: The Harvard Clean Energy Project: large-scale computational screening and design of organic photovoltaics on the world community grid. J Phys Chem Lett 2011, 2:2241-2251.<br><br>[3] O’Boyle NM, Campbell CM, Hutchison GR: Computational design and selection of optimal organic photovoltaic materials. J Phys Chem C 2011, 115:16200-16210.<br><br>[4] Mayr H, Ofial AR: Kinetics of electrophile-nucleophile combinations: a general approach to polar organic reactivity. Pure Appl Chem 2005, 77:1807-1821.<br><br>[5] Kanehisa M, Goto S: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 2000, 28:27-30.<br><br>[6] Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG: ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 2012, 52:1757-1768.<br><br>[7] Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA, Wang J, Yu B, Zhang J, Bryant SH: PubChem Substance and Compound databases. Nucleic Acids Res 2016, 44(D1):D1202-13. <br><br>[8] MOPAC2009 and MOPAC2012, James J. P. Stewart, Stewart Computational Chemistry, Colorado Springs, CO, USA, http://OpenMOPAC.net (2008-2012).<br><br>[9] Schmidt MW, Baldridge KK, Boatz JA, Elbert ST, Gordon MS, Jensen JJ, Koseki S, Matsunaga N, Nguyen KA, Su S, Windus TL, Dupuis M, Montgomery JA: General atomic and molecular electronic structure system. J Comput Chem 1993, 14:1347-1363. GAMESS Version 1 May 2013 (R1).<br><br