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1,805 results for “molecule”
Data presented in "Molecules cooled below the Doppler limit"
<p>Data presented in our paper "Molecules cooled below the Doppler limit". The files give the data that appears in figures 2, 3 and 4 of the paper.</p>
Single molecule experimental data for intensity histograms in Gilburt et al, Angewandte 2017
<p>Raw and partially processed single molecule intensity histogram data for the following publication:</p> <p>James A H Gilburt, Hajrah Sarkar, Peter Sheldrake, Julian Blagg, Liming Ying, Charlotte A Dodson (2017) Dynamic equilibrium of the Aurora-A kinase activation loop revealed by single molecule spectroscopy. <em>Angewandte Chemie</em></p> <p><strong><em>Please cite our publication in any use of this data.</em></strong></p>
Single molecule experimental data for dwell time histogram in Gilburt et al, Angewandte Chemie 2017
<p>Raw and partially processed data for the dwell time histogram in the following publication:</p> <p>James A H Gilburt, Hajrah Sarkar, Peter Sheldrake, Julian Blagg, Liming Ying, Charlotte A Dodson (2017) Dynamic equilibrium of the Aurora-A kinase activation loop revealed by single molecule spectroscopy. <em>Angewandte Chemie</em></p> <p><strong><em>Please cite our publication in any use of this data</em></strong></p>
Calculated state-of-the art results for solvation and ionization energies of thousands of organic molecules relevant to battery design
<p>This dataset presents molecular properties critical for battery electrolyte design, specifically solvation energies, ionization potentials, and electron affinities. The dataset is intended for use in machine learning model testing and algorithm validation. The properties calculated include solvation energies using the COSMO-RS method [1] and ionization potentials and electron affinities using various high-accuracy computational methods as implemented in MOLPRO [2]. Computational details can be found in Ref. [3], with scripts used to generate the data mostly uploaded to our github repository [4].</p> <p>Molecular Datasets Considered:</p> <ul> <li> <p>QM9 Dataset: Contains small organic molecules broadly relevant for quantum chemistry [5]</p> </li> <li> <p>Electrolyte Genome Project (EGP): Focuses on materials relevant to electrolytes.[6]</p> </li> <li> <p>GDB17 and ZINC databases: Offer a broad chemical diversity with potential application in battery technologies. [7, 8]</p> </li> </ul> <h2>Data structure</h2> <p>How to Load the Data:</p> <p>All files can be loaded with</p> <p><br><code>import json</code></p> <p><code>with open("file.json", "r") as f:</code><br><code> data_dict = json.load(f)</code></p> <p><br>and the filestructure can be explored with</p> <p><code>data_dict.keys()</code></p> <p>We have also added an example script in python that shows how to extract all data from the JSON files following this link</p> <p><a href="https://github.com/chemspacelab/VienUppDa/blob/main/SolQuest/BIG_MAP_DATA/load_db.py">How to extract the data</a></p> <p>Note the file structure of the the AMONS JSON files is slightly different as explained below!</p> <h3>Solvation energies</h3> <p>The data is stored in two types of JSON archives: files for full molecules of GDB17 and ZINC and files for amons of GDB17 and ZINC. They are structured differently as amon entries are sorted by the number of heavy atoms in the amon (e.g., all amons with 3 heavy atoms are stored in <code>ni3</code>). Because of the large number of amons with 6 or 7 heavy atoms,they are further split into <code>ni6_1</code>, <code>ni6_2</code>, and so on. A sub dictionary of an amon dictionary or a full molecule dictionary contains the following keys:</p> <p><code>ECFP</code> - ECFP4 representation vector</p> <p><code>SMILES</code> - SMILES string</p> <p><code>SYMBOLS</code> - atomic symbols</p> <p><code>COORDS</code> - atomic positions in Angstrom</p> <p><code>ATOMIZATION</code> - atomization energy in [kcal/mol]</p> <p><code>DIPOLE</code> - dipole moment in Debye</p> <p><code>ENERGY</code> - energy in Hartree</p> <p><code>SOLVATION</code> - solvation energy in [kcal/mol] for different solvents at 300 K.</p> <p> </p> <p>Files:</p> <p> </p> <p><strong><em><code>GDB17.json.zip</code> </em></strong>(unpack with unzip first with unzip <strong><em><code>GDB17.json.zip</code></em></strong>) - subset of GDB17 random molecules</p> <p><strong><em><code>AMONS_ZINC.json</code> </em></strong>-<strong><em> </em></strong>all<strong><em> </em></strong>amons of ZINC up to 7 heavy atoms</p> <p><strong><em><code>EGP.json</code> </em></strong>- EGP molecules</p> <p><code><strong><em>AMONS_GDB17.json</em></strong></code> - all amons of GDB17 up to 7 heavy atoms</p> <p><code><strong>QM9IPEA_raw_molpro_output</strong>.zip</code> - compressed folder with raw Molpro input and output files</p> <table> <tbody> <tr> <td><strong>File Name</strong></td> <td><strong>Description </strong></td> <td><strong>Molecules</strong></td> </tr> <tr> <td>AMONS_GDB17.json</td> <td>GDB17 amons</td> <td>37860</td> </tr> <tr> <td>AMONS_ZINC.json</td> <td>ZINC amons </td> <td>88771</td> </tr> <tr> <td>GDB17.json</td> <td>Subset of GDB17</td> <td>309468</td> </tr> <tr> <td>EGP.json </td> <td>EGP molecules </td> <td>18362</td> </tr> </tbody> </table> <p>Atomic energies $E_{at}$ at BP and def2-TZVPD level in Hartree [Ha]</p> <table> <tbody> <tr> <td><strong>Element</strong></td> <td><strong>H</strong></td> <td><strong>C</strong></td> <td><strong>N</strong></td> <td><strong>O</strong></td> <td><strong>F</strong></td> <td><strong>Br</strong></td> <td><strong>Cl</strong></td> <td><strong>S</strong></td> <td><strong>P</strong></td> </tr> <tr> <td>Eat [Ha]</td> <td>-0.5</td> <td> -37.85</td> <td> -54.60</td> <td> -75.09</td> <td>-99.77</td> <td>-2574.40</td> <td> -460.20</td> <td> -398.16</td> <td>-341.30</td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td><strong>B</strong></td> <td><strong>Si</strong></td> </tr> <tr> <td> -24.65</td> <td> -289.40</td> </tr> </tbody> </table> <p>We follow the convention of negative atomization energies for stablity compared to the isolated atoms:</p> <p>$E_{atomization} = E_{mol} - \sum_{i} E_{at,i}$</p> <p><br>Free energy of solvation at 300 K in [kcal/mol]:</p> <h3>Ionization potentials and electron affinities</h3> <p>The upload contains two JSON files, <strong><em>QM9IPEA.json</em></strong> and <strong><em>QM9IPEA_atom_ens.json</em></strong>. <strong><em>QM9IPEA.json </em></strong>summarizes MOLPRO calculation data grouping it along the following dictionary keys:</p> <p> </p> <p><strong>QM9IPEA.json</strong></p> <p><code>COORDS</code> atom coordinates in Angstroms<br><code>SYMBOLS</code> atom element symbols<br><code>ENERGY</code> total energies for each charge (0, -1, 1) and method considered<br><code>CPU_TIME</code> CPU times (in seconds) spent at each step of each part of the calculation<br><code>DISK_USAGE</code> highest total disk usage in GB<br><code>ATOMIZATION_ENERGY</code> atomization energy at charge 0 (all methods)<br><code>IONIZATION_ENERGY</code> ionization energy for all methods<br><code>ELECTRON_AFFINITY</code> electron affinity for all methods<br><code>HOMO_ENERGY</code> HOMO energy from DFHF calculations<br><code>LUMO_ENERGY</code> LUMO energy from DFHF calculations<br><code>QM9_ID</code> ID of the molecule in the QM9 dataset</p> <p><strong>QM9IPEA_atom_ens.json</strong></p> <p><code>SPINS</code> the spin assigned to elements during calculations of atomic energies<br><code>ENERGY</code> energies of atoms using different methods</p> <p> </p> <p> </p> <p>All energies are given in Hartrees with NaN indicating the calculation failed to converge. Ionization potentials and electron affinities can be recovered as energy differences between neutral and charged (+1 for ionization potentials, -1 for electron affinities) species.</p> <p>"CPU_time" entries contain steps corresponding to individual method calculations, as well as steps corresponding to program operation: "INT" (calculating integrals over basis functions relevant for the calculation), "FILE" (dumping intermediate data to restart file), and "RESTART" (importing restart data). The latter two steps appeared since we reused relevant integrals calculated for neutral species in charged species' calculations; we also used restart functionality to use HF density matrix obtained for the neutral species as the initial density matrix guess for the SCF-HF calculation for charged species. NaN CPU time value means the step was not present or that the calculation is invalid. Note that the CPU times were measured while parallelizing on 12 cores and were not adjusted to single-core.</p> <p><strong> </strong></p> <p><strong><em>QM9IPEA_atom_ens.json</em></strong> contains atomic energies used to calculate atomization energies in <strong><em>QM9IPEA.json</em></strong>, the dictionary keys are:</p> <p><code>SPINS</code> - the spin assigned to elements during calculations of atomic energies.</p> <p><code>ENERGY</code> - energies of atoms using different methods.</p> <p> </p> <p>(Note that H has only one electron and thus does not require a level of theory beyond Hartree-Fock.)</p> <p>NOTE: Additional calculations were performed between publication of arXiv:2308.11196 and creation of this upload. For the version of the dataset used in the manuscript, please refer to DOI:10.5281/zenodo.8252498.</p> <h3>Acknowledgement</h3> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957189 (BIG-MAP) and No. 957213 (BATTERY 2030+). O.A.v.L. has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772834). O.A.v.L. has received support as the Ed Clark Chair of Advanced Materials and as a Canada CIFAR AI Chair. O.A.v.L. acknowledges that this research is part of the University of Toronto’s Acceleration Consortium, which receives funding from the Canada First Research Excellence Fund (CFREF). Obtaining the presented computational results has been facilitated using the queueing system implemented at <a href="https://leruli.com">https://leruli.com</a>. The project has been supported by the Swedish Research Council (Vetenskapsrådet), and the Swedish National Strategic e-Science program eSSENCE as well as by computing resources from the Swedish National Infrastructure for Computing (SNIC/NAISS).</p> <p> </p> <h3>References</h3> <p>[1] Klamt, A.; Eckert, F. COSMO-RS: a novel and efficient method for the a priori prediction of thermophysical data of liquids. Fluid Phase Equilibria 2000, 172, 43–72</p> <p>[2] Werner, H.-J.; Knowles, P. J.; Knizia, G.; Manby, F. R.; Schutz, M. Molpro: a general-purpose quantum chemistry program package. WIREs Comput. Mol. Sci. 2012, 2, 242–253</p> <p>[3] arxiv link of draft</p> <p>[4] <a href="https://github.com/chemspacelab/ViennaUppDa">https://github.com/chemspacelab/ViennaUppDa</a></p> <p>[5] Ramakrishnan, R.; Dral, P. O.; Rupp, M.; von Lilienfeld, O. A. Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 2014, 1, 140022</p> <p>[6] Qu, X.; Jain, A.; Rajput, N. N.; Cheng, L.; Zhang, Y.; Ong, S. P.; Brafman, M.; Mag- inn, E.; Curtiss, L. A.; Persson, K. A. The Electrolyte Genome Project: A big data approach in battery materials discovery. Comput. Mater. Sci. 2015, 103, 56–67</p> <p><strong> </strong>[7] Ruddigkeit, L.; van Deursen, R.; Blum, L. C.; Reymond, J.-L. Enu- meration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17. Journal of Chemical Information and Modeling 2012, 52, 2864–2875</p> <p>[8] Irwin, J. J.; Shoichet, B. K. ZINC A Free Database of Commercially Available Compounds for Virtual Screening. Journal of Chemical Information and Modeling 2005, 45, 177–182.</p>
Allosteric Modulation of YAP/TAZ-TEAD Interaction by Palmitoylation and Small Molecule Inhibitors
<p>The tar file contains 3 directories, each of which contains all of the simulation input files (pdb, prmtop, inpcrd) and trajectory files (nc) for the simulations run. </p><ol><li>TEAD_Only: Simulations of the TEAD protein in the apo, inhibitor(inh)-bound, palmitate(plt)-bound, or palmitic acid(plm)-bound form </li><li>YAP-TEAD: Simulations of the YAP-TEAD heterodimer in the apo, inhibitor(inh)-bound, palmitate(plt)-bound, or palmitic acid(plm)-bound form </li><li>TAZ-TEAD: Simulations of the TAZ-TEAD heterodimer in the apo, inhibitor(inh)-bound, palmitate(plt)-bound, or palmitic acid(plm)-bound form </li></ol>
FragGT: Fragment-based Evolutionary Molecule Generation using Gene Types
<p>This directory contains data requires to run frag-gt (Meyers and Brown, 2023), a fragment-based evolutionary algorithm for generating optimal molecules released as part of the guacamol_baselines GitHub repository – https://github.com/BenevolentAI/guacamol_baselines.</p><p>Scripts for generating the data are available from github. The compressed data directory contains (A) Processed and filtered SMILES derived from ChEMBL v.33 produced by `download_chembl_smiles` and (B) fragment stores generated by `generate_fragstore` and `filter_fragstore` for both the above file in (A) and the original GuacaMol dataset.</p><p>smiles_files and fragstores were derived from molecule data downloaded from ChEMBL (https://www.ebi.ac.uk/chembl).</p><p>Liability: We do not represent and/or warrant that no third party rights exist which might prevent the use of the database or that no third party rights would be infringed by said use.</p><p>(data updated for frag-gt version 0.0.2)</p>
Fig. 2 in What Morphology and Molecules Tell Us about the Evolution of Oligotrichea (Alveolata, Ciliophora)
Fig. 2. Maximum Likelihood tree of the Oligotrichida inferred from small subunit ribosomal RNA (SSU rRNA) gene sequences (66 taxa and 1823 nucleotide positions) aligned with the Muscle algorithm (Edgar 2004) implemented in MEGA ver. 5.1 (Tamura et al. 2011). The alignment is available upon request. The tree was computed with RAxML (Stamatakis et al. 2008) and the datasets were bootstrap re-sampled 100 times. Support values are listed at the nodes. The second values at the nodes represent the posterior probability values of a Bayesian Inference analysis performed with MrBayes (Ronquist and Huelsenbeck 2003). Values below 50% and 0.5, respectively, are represented by a dash. * – initially published as Spirostrombidium sp.; ** – initially published as Parallelostrombidium sp.
Fig. 4 in What Morphology and Molecules Tell Us about the Evolution of Oligotrichea (Alveolata, Ciliophora)
Fig. 4. Evolution of kinetid structures in the somatic ciliature of choreotrichid ciliates. The aloricate taxa have only one kinetid type, except for Leegaardiella elbraechteri and Lynnella. Tintinnids with ventral organelles have two (Tintinnidium, subgenus Tintinnidium), rarely one (Tintinnopsis cylindrata, Membranicola) or three (Tintinnidium, subgenus Semitintinnidium) kinetid types. Extant tintinnids with a ventral kinety have some dikinetids with two cilia and many monokinetids or some dikinetids with two cilia, some dikinetids with one cilium, and many monokinetids.
Fig. 1 in What Morphology and Molecules Tell Us about the Evolution of Oligotrichea (Alveolata, Ciliophora)
Fig. 1. Hypothetical evolution of oligotrichid somatic ciliary patterns (0–IV, VI, VII, after Agatha 2011b; V, VIII–XIV, originals; protargol impregnation). Small arrows mark orientation of kineties (posterior to anterior). Arrowheads denote dorsal breaks in girdle kinety. Dotted arrows mark the tontoniid evolution. Dotted circles denote position of oral primordium in early dividers. Type 0 – dorsal kineties of hypotrich-like ancestor; Type I – strombidiid Parallelostrombidium; Type II – strombidiid Novistrombidium and tontoniid Tontonia; Type III – strombidiid Spirostrombidium; Type IV – strombidiid Omegastrombidium; Type V – strombidiid Strombidium, pelagostrombidiid Limnostrombidium, and tontoniid Pseudotontonia; Type VI – tontoniid Paratontonia; Type VII – tontoniids Laboea and Spirotontonia; Type VIII – strombidiid Foissneridium; Type IX – strombidiid Opisthostrombidium; Type X – cyrtostrombidiid Cyrtostrombidium; Type XI – strombi- diid Williophrya; Type XII – strombidiid Apostrombidium; Type XIII – hypothetic stage; Type XIV – strombidiid Varistrombidium. EX – extrusome attachment sites, GK – girdle kinety, OP – oral primordium, VK – ventral kinety.
Fig. 3 in What Morphology and Molecules Tell Us about the Evolution of Oligotrichea (Alveolata, Ciliophora)
Fig. 3. Maximum Likelihood tree of the Choreotrichida inferred from small subunit ribosomal RNA (SSU rRNA) gene sequences (138 taxa and 1859 nucleotide positions) aligned with the Muscle algorithm (Edgar 2004) implemented in MEGA ver. 5.1 (Tamura et al. 2011). The alignment is available upon request. The tree was computed with RAxML (Stamatakis et al. 2008) and the datasets were bootstrap re-sampled 100 times. Support values are listed at the nodes. The second values at the nodes represent the posterior probability values of a Bayesian Inference analysis performed with MrBayes (Ronquist and Huelsenbeck 2003). Values below 50% and 0.5, respectively, are represented by dashes. Branches with unambiguously clustered taxa are collapsed, species of the genus Tintinnopsis grouped in 5 different clades numbered I–V. Most common lorica structures: – hyaline; – entirely agglomerated; – composed of hyaline collar and agglomerated bowl; * – after Kofoid and Campbell (1929) a synonym of Codonella cratera; ** – does not correspond with the redescription of Agatha and Riedel-Lorjé (2006); *** – possibly incorrectly identified, might be Dadayiella acutiformis; **** – invalid taxon, very likely a replacement lorica (see text).
Multi-cell type deconvolution using a probabilistic model for single-molecule DNA methylation haplotypes
<p>Files required to run deconvolution with CelFIE-ISH and Epistate, in U250 regions from Loyfer et al. 2023, in both "pat" and "epiread" formats. </p>
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
<p>This is the official QH9 datasets from paper 'QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules'. QH9 is a new Quantum Hamiltonian dataset providing precise Hamiltonian matrices for 130,831 stable molecular geometries, based on the QM9 dataset. Here is the QH9Stable dataset which is used in QH-Stable-iid and QH-Stable-ood.</p>
Small molecules targeting the structural dynamics of AR-V7 partially disordered protein using deep learning and physics based models.
<p>Partially disordered proteins can contain both stable and unstable secondary structure segments and are involved in various (mis)functions in the cell. The extensive conformational dynamics of partially disordered proteins scaling with extent of disorder and length of the protein hampers the efficiency of traditional experimental and in-silico structure-based drug discovery approaches. Therefore new efficient paradigms in drug discovery taking into account conformational ensembles of proteins need to emerge. In this study, using as a test case the AR-V7 transcription factor splicing variant related to prostate cancer, we present an automated methodology that can accelerate the screening of small molecule binders targeting partially disordered proteins. By swiftly identifying the conformational ensemble of AR-V7, and reducing the dimension of binding-sites by a factor of 90 by applying appropriate physicochemical filters, we combine physics based molecular docking and multi-objective classification machine learning models that speed up the screening of thousands of compounds targeting AR-V7 multiple binding sites. Our method not only identifies previously known binding sites of AR-V7, but also discovers new ones, as well as increases the multi-binding site hit-rate of small molecules by a factor of 17 compared to naive physics-based molecular docking. </p>
Supporting data for: "Data-driven discovery of cardiolipin-selective small molecules by computational active learning"
<p>This repository contains supporting data and code for the paper titled "Data-driven discovery of cardiolipin-selective small molecules by computational active learning" by Bernadette Mohr, Kirill Shmilovich, Isabel Kleinwächter, Dirk Schneider, Andrew L.Ferguson, and Tristan Bereau.</p>
Improving Stability of Tear Film Lipid Layer via Concerted Action of Two Drug Molecules: A Biophysical View
<p>Surface pressure/area isotherms, stress relaxation transients, molecular dynamic simulation parameters of surface films composed of tear lipids and drug molecules.</p>
Reference data and analysis software for "Four-color single-molecule imaging with engineered tags resolves the molecular architecture of signaling complexes in the plasma membrane"
<p>Reference data set for the single molecule co-tracking analysis presented in "Four-color single-molecule imaging with engineered tags resolves the molecular architecture of signaling complexes in the plasma membrane". Corresponding author for further inquiries:</p> <p>Prof. Dr. Jacob Piehler</p> <p>University of Osnabrück, Department of Biology/Chemistry, Division of Biophysics, Barbarastr. 11, 49076 Osnabrück, Germany</p> <p>https://www.biophysik.uni-osnabrueck.de/</p>
Regulatory spine RS3 residue of protein kinases: a lipophilic bystander or a decisive element in the small-molecule kinase inhibitor binding?
<p>Datasets related to publication: </p> <p>Shevchenko E, Pantsar T: Regulatory spine RS3 residue of protein kinases: a lipophilic bystander or a decisive element in the small-molecule kinase inhibitor binding?. <em><em>Biochem Soc Trans</em></em> 28 February 2022; 50 (1): 633–648</p> <p>https://doi.org/10.1042/bst20210837</p> <p> </p> <p> </p>
Dataset: "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data"
<p>Dataset used in the experiments of the publication: "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data" by Bach et al.</p> <p><strong>File description:</strong></p> <ul> <li> <p>cfmid4.tar: MS² spectra simulated using <a href="https://bitbucket.org/wishartlab/cfm-id-code/src/CFM-ID_4.0.7/">CFM-ID (v4.0.7)</a> for all molecular candidate structures</p> </li> <li> <p>db_layout.png: Visualization of the SQLite database (DB) layout</p> </li> <li> <p>massbank.sqlite.gz: DB containing all needed data to (re-)run the experiments shown in the paper. Please read "DB_README.md" for further details. The database file can be unpacked using gzip.</p> </li> <li> <p>metfrag.tar: MetFrag input files and MS² scores for all candidate sets computed using the <a href="https://ipb-halle.github.io/MetFrag/projects/metfragcl/">MetFrag software</a>.</p> </li> <li> <p>sirius_scores.tar: MS² scores for all candidates and measured spectra using the <a href="https://bio.informatik.uni-jena.de/software/sirius/">SIRIUS software</a>.</p> </li> <li> <p>sirius_inputs.tar: Input (ms-files) for the SIRIUS software.</p> </li> <li> <p>DB_README.md: Description of each table in the "massbank.sqlite" SQLite DB.</p> </li> <li> <p>db_processing_scripts.tar: Scripts to re-produce the "massbank.sqlite" and a README.md providing further information on the process.</p> </li> <li> <p>massbank__2020.11__v0.6.1.sqlite: Base SQLite DB from which the "massbank.sqlite" was build up. It was created using the "<a href="https://github.com/bachi55/massbank2db">massbank2db</a>" (v0.6.1) Python package using the <a href="https://github.com/bachi55/MassBank-data/tree/2020.11-branch">MassBank release 2020.11</a>.</p> </li> <li> <p>substructure_fingerprints.tar: Pre-computed substructure counting fingerprints for all candidates related to our experiments.</p> </li> </ul> <p><strong>Instructions:</strong></p> <p>The "massbank.sqlite" can be directly used with the Structure Support Vector Machine Model (SSVM) described in the manuscript and implemented in the "<a href="https://github.com/aalto-ics-kepaco/msms_rt_ssvm">ssvm</a>" Python package.</p> <p>If desired, the database can be re-produced using the scripts provided in "db_processing_scripts.tar":</p> <ol> <li>Create a directory for all data</li> <li>Download and extract the ... <ol> <li>Processing scripts</li> <li>MS² scorer outputs (e.g. metfrag.tar)</li> <li>Pre-computed substructure fingerprints</li> </ol> </li> <li>Follow the instructions given in the "README.md" of the "db_processing_scripts.tar"</li> </ol>
ePharmaLib: A Versatile Library of e-Pharmacophores to Address Small-Molecule (Poly-)Pharmacology
<p><em><strong>The peer-reviewed publication for this dataset has now been published in Journal of Chemical Information and Modeling, and can be accessed here: <a href="https://doi.org/10.3390/epidemiologia2030024">https://doi.org/10.1021/acs.jcim.1c00135</a>. Please cite this when using the dataset.</strong></em></p> <p><em>Reverse pharmacophore screening (parallel screening) is an efficient and cost-effective computational method used to study the polypharmacology of drugs. To this end, we created ePharmaLib: a collection of 15,148 energetically optimized, structure-based pharmacophores (e-pharmacophores) , with 3 to 8 features constituting 12.6%, 17.9%, 20.9%, 17.1%, 10.6% and 20.9%, respectively. The pharmacophores were generated from the 3D macromolecular structures of druggable proteins in complex with diverse ligands, retrieved from the sc-PDB database (<a href="http://bioinfo-pharma.u-strasbg.fr/scPDB/">http://bioinfo-pharma.u-strasbg.fr/scPDB/</a>). ePharmaLib can either be used with the Schrödinger’s PHASE program (<a href="https://www.schrodinger.com/products/phase">https://schrodinger.com/products/phase</a>) or PHARAO, also known as Align-it (<a href="https://silicos-it.be.s3-website-eu-west-1.amazonaws.com/software/align-it/1.0.4/align-it.html">https://silicos-it.be.s3-website-eu-west-1.amazonaws.com/software/align-it/1.0.4/align-it.html</a>). Designed for drug discovery research, this ready-to-use library could dramatically expedite drug discovery by revealing novel molecular interactions of drugs in an efficient and cost-effective manner.</em></p>
Supplementary material: Picosecond pulse-shaping for strong three-dimensional field-free alignment of generic asymmetric-top molecules
<p><strong>Supplementary material to the manuscript <em>"Picosecond pulse-shaping for strong three-dimensional field-free alignment of generic asymmetric-top molecules"</em> by Terry Mullins, Evangelos T. Karamatskos, Joss Wiese, Jolijn Onvlee, Arnaud Rouzée, Andrey Yachmenev, Sebastian Trippel, and Jochen Küpper, <em>Nat Commun</em> 13, 1431 (2022). <a href="https://doi.org/10.1038/s41467-022-28951-z">https://doi.org/10.1038/s41467-022-28951-z</a>, arXiv: <a href="https://arxiv.org/abs/2009.08157">2009.08157 </a></strong></p> <ul> <li> <em><strong>simulations_part.z*</strong> </em>is split zip archive containing simulations data for indole molecule, such as files with rotational probability density distributions computed at different times <span class="math-tex">\(t=0..1500\)</span> ps during the laser pulse and field-free evolution, and example python scripts for data retrieval.</li> <li><strong><em>rawdata_part.z*</em></strong> is split zip archive containing raw experimental data.</li> <li><strong><em>analysis_scripts.zip</em></strong> is zip archive containing experimental analysis codes.</li> </ul> <p><strong>The <em>simulations_part.zip</em> contains the following files and folders:</strong></p> <ul> <li><em><strong>prob_density_euler_angles</strong></em> contains files <em>rotdens_av_<time>.gz</em> with simulated state-averaged rotational probability density distributions in terms of Euler angles for different times <time>, ranging from the beginning of the alignment laser pulse at <span class="math-tex">\(t=0\)</span> up to <span class="math-tex">\(t=1500\)</span> ps with a time step of 1 ps.<br> Calculations of probability density distributions were done using <a href="https://github.com/CFEL-CMI/richmol">Richmol</a> program.<br> The gzipped ASCII data files <em>rotdens_av_<time>.gz</em> contain in columns the values of the Euler angles <span class="math-tex">\(\phi,\theta,\chi\)</span> followed by the normalized probability density value.</li> <li><em><strong>prob_density_atoms_xyz</strong></em> contains files <em>monte_carlo_av_<time>.h5</em> with state-averaged rotational probability density distributions of all atoms in the indole molecule in terms of their Cartesian coordinates, for different times <time>, ranging from the beginning of the alignment pulse at <span class="math-tex">\(t=0\)</span> up to <span class="math-tex">\(t=1500\)</span> ps with a time step of 1 ps.<br> Structure of <em>monte_carlo_av_<time>.h5</em> HDF5 files:<br> Key Description<br> ----- ----------------<br> 'C10' - Cartesian coordinates of carbon atom no. 10<br> 'C11' - Cartesian coordinates of carbon atom no. 11<br> 'C12' - ...<br> 'C14' - ...<br> 'C3' - ...<br> 'C6' - ...<br> 'C7' - ...<br> 'C9' - ...<br> 'N4' - ...<br> 'H1-C3' - Cartesian coordinates of a vector pointing from carbon atom no. 3 to hydrogen atom no. 1<br> 'H13-C11' - ...<br> 'H15-C12' - ...<br> 'H16-C14' - ...<br> 'H2-N4' - ...<br> 'H5-C7' - ...<br> 'H8-C9' - ...<br> 'ref_vectors' - reference molecular-frame Cartesian coordinates of all atoms<br> 'x' - coordinates of the x-axis of Principal Axes of Inertia Frame<br> 'y' - coordinates of the y-axis of Principal Axes of Inertia Frame<br> 'z' - coordinates of the z-axis of Principal Axes of Inertia Frame<br> 'pol_x' - coordinates of the x-axis of Principal Axes of Polarizability Frame<br> 'pol_y' - coordinates of the y-axis of Principal Axes of Polarizability Frame<br> 'pol_z' - coordinates of the z-axis of Principal Axes of Polarizability Frame</li> <li><em><strong>indole_deflected_states.txt</strong></em> ASCII file contains initial populations of rotational states of indole in the deflected beam.<br> The following data is arranged in columns: <em>m, J</em>, <em>id</em>, <em>energy</em>, <em>normalized population</em>. The <em>J</em> and <em>m</em> are rotational quantum numbers of the total angular momentum and its <em>Z</em>-projection, the <em>id</em> number refers to the state's index in file <em>indole_energies_j0_j20.txt</em> listing rotational states of indole.</li> <li><em><strong>indole_data.py</strong></em> Python module provides basic functions to extract information from HDF5 data files <em>monte_carlo_av_<time>.h5</em>. It can also be used to compute alignment and orientation.</li> <li><em><strong>example_cos.py</strong></em> and <em><strong>example_dens.py</strong></em> Python scripts that demonstrate how to use <em>indole_data.py</em> module for computing and plotting alignment traces and a 2D projection of the probability density distribution, respectively.</li> <li><em><strong>monte_carlo.py</strong></em> Python script that was used to compute through Monte-Carlo sampling probability density distributions for Cartesian positions of atoms in indole (<em>monte_carlo_av_<time>.h5</em> files) using probability density distribution functions in Euler angles (outputs of Richmol program <em>rotdens_av_<time>.gz</em>).</li> </ul> <p><strong>The <em>analysis_scripts.zip</em> contains the following files and folders:</strong></p> <ul> <li><strong><em>H_Plus</em></strong> folder contains codes relevant for the analysis of H<sup>+</sup> ion data. <ul> <li><strong><em>analyse_full_alignment_scans.m</em></strong>: subtracts background and combines delay scan data sets together, takes account of errors.</li> <li><em><strong>calculate_resamped_df.m</strong></em>: called by <em>analyse_full_alignment_scans.m</em> to calculate the frequency sampling.</li> <li><em><strong>unique_mean.m</strong></em>: called by <em>analyse_full_alignment_scans.m</em> when combining data sets. Combines non-unique data points into a single data point.</li> </ul> </li> <li><em><strong>C_Plus2</strong></em> folder contains codes for the analysis of C<sup>2+</sup> ion data. The file descriptions are identical to those in the <em>H_Plus</em> directory.</li> <li><em><strong>intensity/calculate_intensity.m</strong></em>: calculates peak intensity of the laser pulse from measured parameters as well as statistical error.</li> <li><em><strong>intensity/compare_exp_sim.m</strong></em>: fits experimental and theoretical tomography and delay-dependent 2D projection values.</li> <li><em><strong>intensity/nir2hdf5_kHz.py</strong></em>: converts raw data files (from <em>rawdata_part.z*</em> archive<em>)</em> into hdf5 files.</li> </ul>
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