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Prediction of 1-octanol solubilities using data from the Open Notebook Science Challenge

Michael A. Buonaiuto, Andrew S. I. D. Lang
<span title="2015-09-24">2015</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/p7iwk2qc5vd5vjzop4eetdwh7e" style="color: black;">Chemistry Central Journal</a> </i> &nbsp;
Here we extend the range of applicability of 1-octanol solubility models by creating a random forest model that can predict 1-octanol solubilities directly from structure.  ...  Current models are linear in nature and often require foreknowledge of either melting point or aqueous solubility.  ...  Acknowledgements The authors would like to acknowledge the contributors and judges of the Open Notebooks Science Challenge under the leadership of Jean-Claude Bradley without whom this work would not have  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s13065-015-0131-2">doi:10.1186/s13065-015-0131-2</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26435734">pmid:26435734</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4585410/">pmcid:PMC4585410</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gp5vjnd2hndpnjt37jitr7jftm">fatcat:gp5vjnd2hndpnjt37jitr7jftm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190226045223/http://pdfs.semanticscholar.org/68f2/0062e5f540315e7dcee0b0686bec8bc129ec.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/68/f2/68f20062e5f540315e7dcee0b0686bec8bc129ec.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s13065-015-0131-2"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4585410" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

ADME Prediction with KNIME: In silico aqueous solubility models based on supervised recursive machine learning approaches

Gabriela Falcón-Cano, Christophe Molina, Miguel Angel Cabrera-Pérez
<span title="2020-08-08">2020</span> <i title="International Association of Physical Chemists (IAPC)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jw5far46brfrngiixlzeat2ame" style="color: black;">ADMET and DMPK</a> </i> &nbsp;
) are required for predicting aqueous solubility for pharmaceutical molecules.  ...  In-silico prediction of aqueous solubility plays an important role during the drug discovery and development processes.  ...  The application of a consensus regression model and a classification model, using a Random Forest approach, showed good predictive performance in comparison to different models published in the recent  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5599/admet.852">doi:10.5599/admet.852</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35300309">pmid:35300309</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8915604/">pmcid:PMC8915604</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/e32vx3odpnagjc7vwdolb2gt7y">fatcat:e32vx3odpnagjc7vwdolb2gt7y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200819124347/https://pub.iapchem.org/ojs/index.php/admet/article/download/852/1240" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1c/98/1c98611028a231c878f4c521db5ae0d8d1cfabbc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5599/admet.852"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915604" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

ADME prediction with KNIME: A retrospective contribution to the second "Solubility Challenge"

Gabriela Falcón-Cano, Christophe Molina, Miguel Angel Cabrera-Pérez
<span title="2021-07-12">2021</span> <i title="International Association of Physical Chemists (IAPC)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jw5far46brfrngiixlzeat2ame" style="color: black;">ADMET and DMPK</a> </i> &nbsp;
In our previous study, we developed a computational model for aqueous solubility based on recursive random forest approaches.  ...  Since the first "Solubility Challenge", these initiatives have marked the state-of-art of the modelling algorithms used to predict drug solubility.  ...  In our previous publication, we presented a new method based on recursive random forest approaches to predict aqueous solubility values of drug and drug-like molecules [4] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5599/admet.979">doi:10.5599/admet.979</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35300359">pmid:35300359</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8920098/">pmcid:PMC8920098</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4akerbrhgncsjossu5u4mr5dne">fatcat:4akerbrhgncsjossu5u4mr5dne</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210719184847/https://pub.iapchem.org/ojs/index.php/admet/article/download/979/1292" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/30/51/3051aaee31872781b52ce55636c8bbcf03a06ac1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5599/admet.979"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8920098" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Three machine learning models for the 2019 Solubility Challenge

John Mitchell
<span title="2020-06-15">2020</span> <i title="International Association of Physical Chemists (IAPC)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jw5far46brfrngiixlzeat2ame" style="color: black;">ADMET and DMPK</a> </i> &nbsp;
All are founded on tree-like classifiers, with one model being based on Random Forest and another on the related Extra Trees algorithm.  ...  <p class="ADMETabstracttext">We describe three machine learning models submitted to the 2019 Solubility Challenge.  ...  Thus, in 2008 the Journal of Chemical Information and Modeling announced a Solubility Challenge [23] , with entrants invited to predict the newly measured and unrevealed intrinsic aqueous solubilities  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5599/admet.835">doi:10.5599/admet.835</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35300305">pmid:35300305</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8915607/">pmcid:PMC8915607</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/verujg5btvfvxmcyjvrzo4wkdm">fatcat:verujg5btvfvxmcyjvrzo4wkdm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200710073600/http://pub.iapchem.org/ojs/index.php/admet/article/download/835/pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/64/e9/64e9a328aa213466381e0c295a65182961cee3b1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5599/admet.835"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915607" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

Timon Sebastian Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius Ter Laak, Detlev Suelzle, Ursula Ganzer, Nikolaus Heinrich, Klaus-Robert Müller
<span title="">2007</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/eqrkonq3ofesljyzeksgdlfeb4" style="color: black;">Journal of Computer-Aided Molecular Design</a> </i> &nbsp;
We investigate the use of different Machine Learning methods to construct models for aqueous solubility.  ...  Here, we investigate error bars from a Bayesian model (Gaussian Process), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector  ...  We thank Vincent Schütz and Carsten Jahn for maintaining the PCADMET database, and Gilles Blanchard for implementing the random forest method as part of our machine learning toolbox.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10822-007-9160-9">doi:10.1007/s10822-007-9160-9</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/18060505">pmid:18060505</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gwnwmqot6vagxaorgnnc3namsi">fatcat:gwnwmqot6vagxaorgnnc3namsi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200323101823/https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/schroeter_schwaighofer_jcamd_2007_draft.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0f/a4/0fa4559d18133291ece8511cdf6a9e1479f343ef.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10822-007-9160-9"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility

Bowen Tang, Skyler T. Kramer, Meijuan Fang, Yingkun Qiu, Zhen Wu, Dong Xu
<span title="2020-02-21">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5aubiwi6v5beng6iqzj577kiaa" style="color: black;">Journal of Cheminformatics</a> </i> &nbsp;
Further, SAMPN outperforms random forests and the deep learning framework MPN from Deepchem.  ...  In addition, another formulation of SAMPN (Multi-SAMPN) can simultaneously predict multiple chemical properties with higher accuracy and efficiency than other models that predict one specific chemical  ...  Random forest To compare our SAMPN method with the traditional machine learning methods, we chose a random forest model as the baseline.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s13321-020-0414-z">doi:10.1186/s13321-020-0414-z</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33431047">pmid:33431047</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/q7a4tjja2zcpzmjv5leqa3g63m">fatcat:q7a4tjja2zcpzmjv5leqa3g63m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200223063015/https://jcheminf.biomedcentral.com/track/pdf/10.1186/s13321-020-0414-z" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ea/af/eaaf53202a815c6f6dbafddf1e873778921da90e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s13321-020-0414-z"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a>

Prediction of CO2 solubility in deep eutectic solvents using random forest model based on COSMO-RS-derived descriptors

Jingwen Wang, Zhen Song, Lifang Chen, Tao Xu, Liyuan Deng, Zhiwen Qi
<span title="">2021</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/safw25tztjhhljpguxvdxm5bau" style="color: black;">Green Chemical Engineering</a> </i> &nbsp;
For example, Saghafi et al. developed a QSPR model J o u r n a l P r e -p r o o f for the prediction of CO2 solubility in aqueous solution of diethanolamine plus methyldiethanolamine based on random forest  ...  Taking account of the essential aspects mentioned above, this work aims to develop a predictive QSPR model for CO2 solubility in DESs with random forest based on the COSMO-RS-derived descriptors.  ...  Table 6 Comparison of random forest-based QSPR and COSMO-RS models for CO2 solubility in DESs.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.gce.2021.08.002">doi:10.1016/j.gce.2021.08.002</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zz6wtuc5zbeijjcowxxh5dyo6a">fatcat:zz6wtuc5zbeijjcowxxh5dyo6a</a> </span>
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ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches

Ashok K. Sharma, Gopal N. Srivastava, Ankita Roy, Vineet K. Sharma
<span title="2017-11-30">2017</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qigargnicncadmdn56ei23yjnu" style="color: black;">Frontiers in Pharmacology</a> </i> &nbsp;
partial least squares based regression model for the prediction of permeability (caco-2) performed better (R 2 = 0.68) in comparison to the random forest and MLR based regression models.  ...  Random forest based regression model for the prediction of solubility performed better (R 2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the  ...  Multilinear regression (MLR), Random Forest Regression (RFR) and Partial Least Square Regression (PLSR) were optimized to calculate the aqueous solubility and caco-2 cell permeability (Schneider et al  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fphar.2017.00880">doi:10.3389/fphar.2017.00880</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29249969">pmid:29249969</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5714866/">pmcid:PMC5714866</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ziuwkjk3ebeita5atjivl5ugwq">fatcat:ziuwkjk3ebeita5atjivl5ugwq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190222041827/http://pdfs.semanticscholar.org/3356/87a549b8ed62aca01645891aa7caf5b8e5d3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/33/56/335687a549b8ed62aca01645891aa7caf5b8e5d3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fphar.2017.00880"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> frontiersin.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5714866" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Machine learning in prediction of intrinsic aqueous solubility of drug‐like compounds: Generalization, complexity, or predictive ability?

Mario Lovrić, Kristina Pavlović, Petar Žuvela, Adrian Spataru, Bono Lučić, Roman Kern, Ming Wah Wong
<span title="2021-05-07">2021</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mgvnagfuwjddxbu2ryhv4mxcii" style="color: black;">Journal of Chemometrics</a> </i> &nbsp;
We present a collection of publicly available intrinsic aqueous solubility data of 829 drug-like compounds.  ...  Four different machine learning algorithms (random forests [RF], LightGBM, partial least squares, and least absolute shrinkage and selection operator [LASSO]) coupled with multistage permutation importance  ...  Many other research groups also dealt with the solubility prediction challenge, attempting to predict both logS w (aqueous solubility; measured at a certain pH) and logS 0 (intrinsic solubility; solubility  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/cem.3349">doi:10.1002/cem.3349</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cxkciqcgg5azroc3kz7rt24qxe">fatcat:cxkciqcgg5azroc3kz7rt24qxe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715214126/https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cem.3349" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5c/92/5c9285c00137e5d01d714b7223529ded395c86e3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/cem.3349"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> wiley.com </button> </a>

Do you know your r2?

Alex Avdeef
<span title="2020-08-30">2020</span> <i title="International Association of Physical Chemists (IAPC)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jw5far46brfrngiixlzeat2ame" style="color: black;">ADMET and DMPK</a> </i> &nbsp;
This commentary briefly reviews the definitions of three types of r2 and RMSE statistics (model validation, bias compensation, and Pearson) and how systematic errors due to shortcomings in solubility prediction  ...  Popular statistics to indicate the strength of the prediction model include the coefficient of determination (r2), Pearson's linear correlation coefficient (rPearson), and the root-mean-square error (RMSE  ...  Prediction of aqueous intrinsic solubility of druglike molecules using Random Forest regression trained with Wiki-pS0 database.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5599/admet.888">doi:10.5599/admet.888</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35299878">pmid:35299878</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8923304/">pmcid:PMC8923304</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vkrzirs245fudlnxan6wwfde3y">fatcat:vkrzirs245fudlnxan6wwfde3y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201210104113/https://pub.iapchem.org/ojs/index.php/admet/article/download/888/1242" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/75/18/751845910d0ad4b3fb12ba25bb19b5345ad38a3b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5599/admet.888"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8923304" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Modeling physico-chemical ADMET endpoints with multitask graph convolutional networks

Floriane Montanari, Lara Kuhnke, Antonius Ter Laak, Djork-Arné Clevert
<span title="2021-04-28">2021</span> <i title="figshare"> figshare.com </i> &nbsp;
The new model shows increased predictive performance on all endpoints compared to previous modeling methods.  ...  Here, we collected all the Bayer in house data related to these properties and applied machine learning techniques to predict them for new compounds.  ...  For this, we used the predictions of the model for melting point and LogD, obtained the aqueous solubilities according to the Yalkowsky equation and compared these with the predicted LOO.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.6084/m9.figshare.14499807.v1">doi:10.6084/m9.figshare.14499807.v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jsu4h4scbvcrjfhrtppo7srti4">fatcat:jsu4h4scbvcrjfhrtppo7srti4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429182438/https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/27773967/MTNN_paper_ChemRxiv_2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/29/38/293842716769e2c7b4abad49bba52601df9a702f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.6084/m9.figshare.14499807.v1"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> figshare.com </button> </a>

Solubility prediction in the bRo5 chemical space: where are we right now?

Giuseppe Ermondi, Vasanthanathan Poongavanam, Maura Vallaro, Jan Kihlberg, Giulia Caron
<span title="2020-07-08">2020</span> <i title="International Association of Physical Chemists (IAPC)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jw5far46brfrngiixlzeat2ame" style="color: black;">ADMET and DMPK</a> </i> &nbsp;
Currently, methods for modelling of solubility will have to be tailored to the set of investigated compounds.  ...  Modelling the solubility of compounds in the "beyond Rule of 5" (bRo5) chemical space is in its infancy and to date only a few studies have been reported in the literature.  ...  The GSE and the Abraham Solvation Equation failed to predict the solubility of the larger compounds in bRo5 space, whereas the Random Forest Regression (RFR) method provided better results.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5599/admet.834">doi:10.5599/admet.834</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35300306">pmid:35300306</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8915608/">pmcid:PMC8915608</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/0c47ac79bec0483bac88824e0014f385">doaj:0c47ac79bec0483bac88824e0014f385</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qn7tmjrpfbcr7iac3e2w34aebu">fatcat:qn7tmjrpfbcr7iac3e2w34aebu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201106120931/https://pub.iapchem.org/ojs/index.php/admet/article/download/834/pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/68/d8/68d89961c4563cb3416d42c5bbef1690f525579c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5599/admet.834"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915608" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

TopP-S: Persistent homology based multi-task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility [article]

Kedi Wu, Zhixiong Zhao, Renxiao Wang, Guo-Wei Wei
<span title="2017-12-09">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Accurate theoretical prediction of aqueous solubility and partition coefficient plays an important role in drug design and discovery.  ...  Aqueous solubility and partition coefficient are important physical properties of small molecules.  ...  To predict partition coefficient and aqueous solubility, we integrate ESPH with advanced machine learning methods, including gradient boosting tree, random forest, and deep neural networks to construct  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1801.01558v1">arXiv:1801.01558v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cqidbn4c4rcd5hwaetkafbjeie">fatcat:cqidbn4c4rcd5hwaetkafbjeie</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191018095618/https://arxiv.org/pdf/1801.01558v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9d/c6/9dc6268422e21de5fbc401ce6c55a11cc7bfedbe.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1801.01558v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional Networks

Floriane Montanari, Lara Kuhnke, Antonius Ter Laak, Djork-Arné Clevert
<span title="2019-12-21">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dstyyzbt45gknhqqjsh45p55h4" style="color: black;">Molecules</a> </i> &nbsp;
The new model shows increased predictive performance compared to previous modeling methods and will allow early prioritization of compounds even before they are synthesized.  ...  In addition, our model follows the generalized solubility equation without being explicitly trained under this constraint.  ...  For this, we used the predictions of the model for melting point and LogD, obtained the aqueous solubilities according to the Yalkowsky equation and compared these with the predicted LOO.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/molecules25010044">doi:10.3390/molecules25010044</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31877719">pmid:31877719</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6982787/">pmcid:PMC6982787</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4jfr73hkd5hvhbzilp56vkmb64">fatcat:4jfr73hkd5hvhbzilp56vkmb64</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191224012125/https://res.mdpi.com/d_attachment/molecules/molecules-25-00044/article_deploy/molecules-25-00044.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4f/b4/4fb438db873f720b3b17f961a82722a9f6464996.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/molecules25010044"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6982787" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Can small drugs predict the intrinsic aqueous solubility of 'beyond Rule of 5' big drugs?

Alex Avdeef, Manfred Kansy
<span title="2020-04-25">2020</span> <i title="International Association of Physical Chemists (IAPC)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jw5far46brfrngiixlzeat2ame" style="color: black;">ADMET and DMPK</a> </i> &nbsp;
The Random Forest regression (RFR) method predicts solubility more accurately, albeit in the manner of a 'black box.'  ...  <p class="ADMETabstracttext">The aim of the study was to explore to what extent small molecules (mostly from the Rule of 5 chemical space) can be used to predict the intrinsic aqueous solubility, S<sub  ...  Random Forest regression Of the new machine-learning statistical approaches, the Random Forest regression (RFR) method is thought to be one of the most accurate in predicting solubility [17] [18] [19]  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5599/admet.794">doi:10.5599/admet.794</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35300304">pmid:35300304</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8915605/">pmcid:PMC8915605</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dmtxiljm3vet7jxjot3p3womd4">fatcat:dmtxiljm3vet7jxjot3p3womd4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200505021223/http://pub.iapchem.org/ojs/index.php/admet/article/download/794/pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/80/5f/805f0dfacb2cbf0b09dd4883f7e3e4ac17120f87.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5599/admet.794"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915605" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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