Efficient Drug Discovery with LSTM-Based Models: Insights from SARS-CoV-2 Variants

Efficient Drug Discovery with LSTM-Based Models: Insights from SARS-CoV-2 Variants | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Efficient Drug Discovery with LSTM-Based Models: Insights from SARS-CoV-2 Variants Monchi Estevez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6387972/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The rapid evolution of SARS-CoV-2 variants underscored the need for accelerated drug discovery methods. This study demonstrates the use of recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) units to generate novel pharmaceutical compounds capable of inhibiting SARS-CoV-2 through protein binding, using variants (Alpha, Beta, Gamma, Delta) as reference points. Three LSTM-based RNN models were developed, trained on a dataset of 2,572,812 preprocessed SMILES (Simplified Molecular-Input Line-Entry System) sequences from the ChEMBL 29 and MOSES databases, and fine-tuned against these variants. The models, differing in dropout regularization parameters, were evaluated for validity, originality, and uniqueness of generated molecules, with performance assessed via simulated protein binding affinity scores using PyRx. Results demonstrate that Model 3, with the lowest dropout values (0.2 and 0.4), outperformed others, achieving a 98.0% validity rate, 94.1% originality, and 97.9% uniqueness, and generating molecules with high binding affinities (e.g., -17.40 kcal/mol). These findings highlight the efficacy of LSTM-RNNs in automating and optimizing drug discovery, potentially offering a scalable, efficient alternative to traditional methods. Further laboratory validation is recommended to translate these computational results into practical therapeutic applications. Artificial Intelligence and Machine Learning lstm rnn smiles drug discovery sars-cov-2 Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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