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Drug discovery with explainable artificial intelligence

José Jiménez-Luna, Francesca Grisoni, Gisbert Schneider
<span title="">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/v66j35cgxvajrnw3y4tkpw4ine" style="color: black;">Nature Machine Intelligence</a> </i> &nbsp;
While there have been efforts to explain QSARs in terms of algorithmic insights and molecular descriptor analysis 14-19 , deep neural network models notoriously elude immediate accessibility by the human  ...  Certain deep learning models have been shown to match or even exceed the performance of the familiar existing machine learning and quantitative structure-activity relationship (QSAR) methods for drug discovery  ...  Methods based on graph convolution represent a powerful tool in drug discovery due to their immediate and natural connection with representations that are intuitive to chemists (that is, molecular graphs  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s42256-020-00236-4">doi:10.1038/s42256-020-00236-4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nlkwpc2jvvhcblmiulbdzzxaiq">fatcat:nlkwpc2jvvhcblmiulbdzzxaiq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201215234520/https://www.nature.com/articles/s42256-020-00236-4.pdf?error=cookies_not_supported&amp;code=6249f2b3-5d14-4e54-876c-0bf04b8ec400" 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/6d/bf/6dbf952d566831d8c3f2a982a91bef324ec30069.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s42256-020-00236-4"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> nature.com </button> </a>

Drug discovery with explainable artificial intelligence [article]

José Jiménez-Luna, Francesca Grisoni, Gisbert Schneider
<span title="2020-07-02">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Deep learning bears promise for drug discovery, including advanced image analysis, prediction of molecular structure and function, and automated generation of innovative chemical entities with bespoke  ...  There is a demand for 'explainable' deep learning methods to address the need for a new narrative of the machine language of the molecular sciences.  ...  A crucial challenge for future AI-assisted drug discovery is the data representation used for machine and deep learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.00523v2">arXiv:2007.00523v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vwbm5ctaengetbsrkqjf54hoei">fatcat:vwbm5ctaengetbsrkqjf54hoei</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200710051603/https://arxiv.org/pdf/2007.00523v2.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/d3/a8/d3a857a4bccf9a23b2d1e523c89456de728403b3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.00523v2" 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>

Advances and Perspectives in Applying Deep Learning for Drug Design and Discovery

Celio F. Lipinski, Vinicius G. Maltarollo, Patricia R. Oliveira, Alberico B. F. da Silva, Kathia Maria Honorio
<span title="2019-11-05">2019</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/t4zwwbshrrfd3hjbg4s3bysm7q" style="color: black;">Frontiers in Robotics and AI</a> </i> &nbsp;
Therefore, in this mini-review we will briefly outline the present scope, advances, and challenges related to the use of DL in drug design and discovery, describing successful studies involving quantitative  ...  In particular, artificial neural networks have been successfully applied in medicinal chemistry studies.  ...  ACKNOWLEDGMENTS The authors would like to thank FAPESP-IBM (2016/18840-3), FAPESP (2016/24524-7), Pró-Reitoria de Pesquisa-Universidade de São Paulo (USP), CNPq, and CAPES for funding.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/frobt.2019.00108">doi:10.3389/frobt.2019.00108</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33501123">pmid:33501123</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7805776/">pmcid:PMC7805776</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/osh2gfsr5vfufhokxe4voxauhi">fatcat:osh2gfsr5vfufhokxe4voxauhi</a> </span>
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Comprehensive Survey of Recent Drug Discovery Using Deep Learning

Jintae Kim, Sera Park, Dongbo Min, Wankyu Kim
<span title="2021-09-15">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3loumxx7kzamnlu4h6x3xoz6ay" style="color: black;">International Journal of Molecular Sciences</a> </i> &nbsp;
In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing.  ...  The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest.  ...  With the development of graph neural networks, recent DL-based works have adopted molecular graphs as drug or target representations for both DTI prediction models and novel molecular design models.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/ijms22189983">doi:10.3390/ijms22189983</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34576146">pmid:34576146</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8470987/">pmcid:PMC8470987</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yji6q3cf4fb7ha6m6f5bxcvamq">fatcat:yji6q3cf4fb7ha6m6f5bxcvamq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210918065041/https://mdpi-res.com/d_attachment/ijms/ijms-22-09983/article_deploy/ijms-22-09983.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/46/4e/464e92844ed60de5be319498f5f62d5dead4e5bd.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/ijms22189983"> <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/PMC8470987" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

A renaissance of neural networks in drug discovery

Igor I. Baskin, David Winkler, Igor V. Tetko
<span title="2016-07-04">2016</span> <i title="Informa UK Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4m2squtugrdvtgjdvfbz5jhmaq" style="color: black;">Expert Opinion on Drug Discovery</a> </i> &nbsp;
Neural networks are becoming a very popular method for solving machine learning and 10 artificial intelligence problems.  ...  physicochemical properties; characteristics of drug-delivery systems and virtual screening. 20 Expert opinion: Neural networks continue to grow in importance for drug discovery.  ...  involves formation of different levels of data representation • Deep neural networks could particularly be useful for analyzing huge amounts of chemical and biological information for drug discovery,  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1080/17460441.2016.1201262">doi:10.1080/17460441.2016.1201262</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/27295548">pmid:27295548</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2cbfrf6jbzbolkxkriudetdfm4">fatcat:2cbfrf6jbzbolkxkriudetdfm4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190428174943/https://push-zb.helmholtz-muenchen.de/deliver.php?id=21243" 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/7a/c8/7ac85d1a91a57c0b0ec72eeb763cc356362aeff2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1080/17460441.2016.1201262"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> tandfonline.com </button> </a>

Chemoinformatics and Drug Discovery

Jun Xu, Arnold Hagler
<span title="2002-08-30">2002</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dstyyzbt45gknhqqjsh45p55h4" style="color: black;">Molecules</a> </i> &nbsp;
This article reviews current achievements in the field of chemoinformatics and their impact on modern drug discovery processes.  ...  The applications of cheminformatics in drug discovery, such as compound selection, virtual library generation, virtual high throughput screening, HTS data mining, and in silico ADMET are discussed.  ...  Richard Shaps for his comments and advice.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/70800566">doi:10.3390/70800566</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vsf3iljr45cy3j7qadvq35pbcq">fatcat:vsf3iljr45cy3j7qadvq35pbcq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170828232641/http://biochem158.stanford.edu/Drug%20Discovery/Chemoinformatics.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/6b/d3/6bd3a92d1d6ee1f18024e322a159f356de5e44dc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/70800566"> <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>

Exploring chemical space using natural language processing methodologies for drug discovery

Hakime Öztürk, Arzucan Özgür, Philippe Schwaller, Teodoro Laino, Elif Ozkirimli
<span title="2020-02-03">2020</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q2rzh4rosrd2rhrlmpfocas4qa" style="color: black;">Drug Discovery Today</a> </i> &nbsp;
Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge.  ...  entities and then use it to construct models to predict molecular properties or to design novel molecules.  ...  [118] showed that seq2seq models could compete with graph neural network-based models in the reaction prediction task [119] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.drudis.2020.01.020">doi:10.1016/j.drudis.2020.01.020</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32027969">pmid:32027969</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5dhdhn5pxrffnegbqf73cym3kq">fatcat:5dhdhn5pxrffnegbqf73cym3kq</a> </span>
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Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

Hyunho Kim, Eunyoung Kim, Ingoo Lee, Bongsung Bae, Minsu Park, Hojung Nam
<span title="2021-01-07">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ppe5thsjq5dg3hyqv3nld63y6u" style="color: black;">Biotechnology and Bioprocess Engineering</a> </i> &nbsp;
Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development.  ...  In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.  ...  The authors declare no conflict of interest. Neither ethical approval nor informed consent was required for this study.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s12257-020-0049-y">doi:10.1007/s12257-020-0049-y</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33437151">pmid:33437151</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7790479/">pmcid:PMC7790479</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wqdmkkas2nb65gy3pymlgisuwi">fatcat:wqdmkkas2nb65gy3pymlgisuwi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429075946/https://link.springer.com/content/pdf/10.1007/s12257-020-0049-y.pdf?error=cookies_not_supported&amp;code=4d695320-b567-453d-b30f-90ab54fe7f47" 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/ca/7e/ca7e8bc95864805c15e667ad016dfff98865fd51.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s12257-020-0049-y"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790479" 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 approaches in drug discovery: methods and applications

Antonio Lavecchia
<span title="">2015</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q2rzh4rosrd2rhrlmpfocas4qa" style="color: black;">Drug Discovery Today</a> </i> &nbsp;
In addition, I analyze several relevant VS studies from recent publications, providing a detailed view of the current state-of-the-art in this field and highlighting not only the problematic issues, but  ...  Here, I focus on machine-learning techniques within the context of ligand-based VS (LBVS).  ...  Acknowledgments I dedicate this work to the memory of my uncle, Francesco Lavecchia, who passed away on 1 May 2014, while this article was being prepared.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.drudis.2014.10.012">doi:10.1016/j.drudis.2014.10.012</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25448759">pmid:25448759</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6hm6fyziq5f5zg3ejvluclyqvq">fatcat:6hm6fyziq5f5zg3ejvluclyqvq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808084627/http://csmres.co.uk/cs.public.upd/article-downloads/Machine-learning-approaches-in-drug-discovery-methods-and-applications.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/43/20/4320c4b1b91c6915f1699e962d06853fd0d9b451.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.drudis.2014.10.012"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

AMPL: A Data-Driven Modeling Pipeline for Drug Discovery [article]

Amanda J. Minnich, Kevin McLoughlin, Margaret Tse, Jason Deng, Andrew Weber, Neha Murad, Benjamin D. Madej, Bharath Ramsundar, Tom Rush, Stacie Calad-Thomson, Jim Brase, Jonathan E. Allen
<span title="2019-11-14">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
As a result of these comprehensive experiments, we have found that physicochemical descriptors and deep learning-based graph representations significantly outperform traditional fingerprints in the characterization  ...  One of the key requirements for incorporating machine learning into the drug discovery process is complete reproducibility and traceability of the model building and evaluation process.  ...  An extensive set of experiments were conducted with AMPL, and key observations include: • Physicochemical descriptors and deep learning-based graph representations are significantly better than traditional  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1911.05211v2">arXiv:1911.05211v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yo4smamvfrgnjn2cbv6nebir4i">fatcat:yo4smamvfrgnjn2cbv6nebir4i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200928210345/https://arxiv.org/pdf/1911.05211v2.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/e7/52/e75219db6aacf23016bc79416d7119a405076854.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1911.05211v2" 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>

A review on Machine Learning approaches and trends in drug discovery

Paula Carracedo-Reboredo, Jose Linares-Blanco, Nereida Rodriguez-Fernandez, Francisco Cedron, Francisco J. Novoa, Adrian Carballal, Victor Maojo, Alejandro Pazos, Carlos Fernandez-Lozano
<span title="2021-08-12">2021</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cmflnsn2l5gylcviv447te3mku" style="color: black;">Computational and Structural Biotechnology Journal</a> </i> &nbsp;
This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.  ...  Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies.  ...  To avoid this fact, in [64] develop a new graphical neural network architecture for molecular representation that uses a graphical attention mechanism to learn from relevant drug discovery data sets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.csbj.2021.08.011">doi:10.1016/j.csbj.2021.08.011</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34471498">pmid:34471498</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8387781/">pmcid:PMC8387781</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s5pwhypudfehbotkrofqgbq33m">fatcat:s5pwhypudfehbotkrofqgbq33m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210814114151/https://pdf.sciencedirectassets.com/311228/AIP/1-s2.0-S2001037021003421/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEAQaCXVzLWVhc3QtMSJHMEUCIH%2BongerbZxHbHMfkX3eDQvMJW3exqGKbnqe3tcKACufAiEA1IoWUcx0on7LnIp0Ca24mm2u4%2FKEuStxqtzEf4ZpFtIq%2BgMILRAEGgwwNTkwMDM1NDY4NjUiDOcl6zMAICuF%2FgnHbSrXA3g6XST66KD8hIV1W%2FsE5uyqBs2WrWXRm%2BMAZJffir1G4CmZy4GtmfNv7HZ5bIRnJPeJUOlxzM924Bvpw2wOyYzxkCieEoTQxAjIgM99I3jaHQ%2F9NSdslePfPU6w%2B0H0Cs3kLhsk9gx5xPRrdjTdDBnMbhaYKjQdZvtefGAkWMx5hAmIseJOjpfOOnRvC1BWHNpYEW7foy5%2BfyeFY4odKxty4n4TohcR%2Bqozu3mN0ffhWIkn881UBST4sNyke8BPmAslm2KmNZ09kG8X%2Fiy2KNWgIBK8MT4fWSblS41xplFEsTM3RPkZWcZAmqSfk0IgZmtnsZ7oD20rWnI6Rwoi7Y9cQQZHK%2FNm488nFy728MlkT0eMilVyhcvQL9hblrBd4fCDvtUrEmoVAYDXfT%2BrGDoNpzpD9vqu3psb4mEjlY%2FP%2B%2BkZbzMot6iuWgmelSyhfuxFEpxXQlfL9KNejsw8ngGVvRlqGmHlWuL6TyX1YKtLxR5108GeQTwzUi9Bq94VCBCInjtsbBLtpwJWlffQAFccmufOVvmeVtWSqHq3Bx0POmWffQReo9YlR3Acs2aaVzeVMNYjzCMnr7ZzZRRh3GEXM1HBrN7RCZBydc2rZ2VJLhocs%2FsfmDCi0d6IBjqlATq%2FWNu7tfReJJZH0FgbdylVDtT087iPonmobIYm4x95HvuAlOJxTsDzN5gIz39bYwE%2FJmkxu5brDOGcN9EE5BsT%2BESd2A5vhNfhDWbwMme%2Fz2ABARwimhq%2Bw%2BHv6RgrUrvgOTGPgHvmgP%2BZDnnxkfNYBdTWP4kYI3HO5rE%2BHxzn7HPrKl0eghg7ycxo2YSkbvyZXOrDCXI8IJ0zgj4T2409kBKXYg%3D%3D&amp;X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Date=20210814T114146Z&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Expires=300&amp;X-Amz-Credential=ASIAQ3PHCVTY62GZTERK%2F20210814%2Fus-east-1%2Fs3%2Faws4_request&amp;X-Amz-Signature=34284b5506261c1fb8691d12c1e92453fc9aa23b905340c2056cc03d0c1c9ef3&amp;hash=4e24151c84e4b9201d9b1d556fcf2900b712a39ef59e8eb72e3e9058fd61693b&amp;host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&amp;pii=S2001037021003421&amp;tid=spdf-465602e5-ac37-4858-9b86-f5f0108aa2c4&amp;sid=da46bb0f557791464f7b68b8049ff496f31egxrqa&amp;type=client" 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/4b/e8/4be8cbf296cc881ffca55dabecf32cb7a671eef1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.csbj.2021.08.011"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8387781" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Learn molecular representations from large-scale unlabeled molecules for drug discovery [article]

Pengyong Li, Jun Wang, Yixuan Qiao, Hao Chen, Yihuan Yu, Xiaojun Yao, Peng Gao, Guotong Xie, Sen Song
<span title="2020-12-21">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
How to produce expressive molecular representations is a fundamental challenge in AI-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for modeling molecular data.  ...  Here, we proposed a novel Molecular Pre-training Graph-based deep learning framework, named MPG, that leans molecular representations from large-scale unlabeled molecules.  ...  Recently, among the promising deep learning architectures, graph neural network (GNN) has gradually emerged as a powerful candidate for modeling molecular data [11] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2012.11175v1">arXiv:2012.11175v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tnxekktvbfc6ro4alanzglc3fa">fatcat:tnxekktvbfc6ro4alanzglc3fa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201225063509/https://arxiv.org/pdf/2012.11175v1.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/e5/2b/e52bb04d8ecad05d0cde8a57af94478c0b904712.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2012.11175v1" 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>

Low Data Drug Discovery with One-shot Learning [article]

Han Altae-Tran, Bharath Ramsundar, Aneesh S. Pappu, Vijay Pande
<span title="2016-11-10">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery.  ...  We introduce a new architecture, the residual LSTM embedding, that, when combined with graph convolutional neural networks, significantly improves the ability to learn meaningful distance metrics over  ...  Thanks to David Duvenaud for useful preliminary discussions. B.R. was supported by the Fannie and John Hertz Foundation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1611.03199v1">arXiv:1611.03199v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hzeuiulzlfaoxa75qiiwo7osie">fatcat:hzeuiulzlfaoxa75qiiwo7osie</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191013031427/https://arxiv.org/pdf/1611.03199v1.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/53/0b/530b755651ddd136a55effc5aeb58239b7b49df6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1611.03199v1" 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>

Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition [article]

Sebastian Raschka, Benjamin Kaufman
<span title="2020-06-06">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
architectures and feature representations of molecular data.  ...  This review aims to summarize AI-based research for GPCR bioactive ligand discovery with a particular focus on the most recent achievements and research trends.  ...  Acknowledgements Support for this work was provided by the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin-Madison with funding from the Wisconsin Alumni  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2001.06545v3">arXiv:2001.06545v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/e5f4v3fnyvdwtliftwia6rwyc4">fatcat:e5f4v3fnyvdwtliftwia6rwyc4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200610074625/https://arxiv.org/pdf/2001.06545v3.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] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2001.06545v3" 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>

AI – New Avenue for Drug Discovery and Optimization

Rupesh Dudhe, Anshu Chaudhary Dudhe, Rupesh Dudhe, Suhas N. Sakarkar, Omji Porwal
<span title="2021-01-30">2021</span> <i title="Science Repository OU"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/awr2ojqqpba3rmse3n6cgikbge" style="color: black;">Clinical Oncology and Research</a> </i> &nbsp;
Selection of the suitable drug for a patient typically requires the patient data, such as genetics or proteomics, with drug data, like compound chemical descriptors, to score the therapeutic efficacy of  ...  Deciding the dosage schedule for administration of drugs is performed using mathematical models to interpret pharmacokinetic and pharmacodynamics data.  ...  Our deep neural network model works by building a molecular representation based on a specific property, in our case the inhibition of the growth of E. coli, using a directed message passing approach.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.31487/j.cor.2021.01.02">doi:10.31487/j.cor.2021.01.02</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rdfx3b64cjevhkbl5ea4rdubfq">fatcat:rdfx3b64cjevhkbl5ea4rdubfq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429043222/https://www.sciencerepository.org/articles/ai-new-avenue-for-drug-discovery-and-optimization_COR-2021-1-102.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/16/02/160283030543d80ef757d59c030a4e5ea113e285.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.31487/j.cor.2021.01.02"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>
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