Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Efficient lipophilicity prediction of molecules employing deep-learning models Subject The topic of the resource Convolutional neural network; Deep learning; Drug design; Lipophilicity; Long-short term memory; Multi-layer perceptron Description An account of the resource Lipophilicity, expressed as logP, is a significant physiochemical property and is an indicator of absorption, distribution, metabolism and elimination characteristics of drugs used in medication. It is one of the major deciding factors of the fate of a molecule to be a successful drug. Mol2vec is a convenient and unsupervised machine learning technique which produces high-dimensional vector representations of molecules and its molecular substructures. The work described here aims to simplify prediction of logP values with high-degree of accuracy by using Deep Learning (DL) models paired with Mol2vec. The work described in this paper empirically demonstrates that by using the described DL models paired with Mol2vec, one can achieve results which are much better than the conventional ML techniques as well as more complex and recent algorithms like Message-passing Neural Networks (MPNN), Graph Convolution (GC) and Spatial Graph embedding (C-SGEN). Our RMSE (Root Mean Square Error) scores from the ensemble model is one of the best reported so far in literature. The methods elaborated in this paper are simple, yet effective in predicting logP values to a great degree of accuracy due to the use of Mol2vec and standard TensorFlow operators. The models employed here can be coded and maintained with much more ease compared to the techniques of MPNN, C-SGEN or GC. 2021 Elsevier B.V. Creator An entity primarily responsible for making the resource Datta R.; Das D.; Das S. Source A related resource from which the described resource is derived Chemometrics and Intelligent Laboratory Systems, Vol-213 Publisher An entity responsible for making the resource available Elsevier B.V. Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.chemolab.2021.104309" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.chemolab.2021.104309</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104961606&amp;doi=10.1016%2Fj.chemolab.2021.104309&amp;partnerID=40&amp;md5=f4c3a6a54a3a7c8a1d49ff40cdab3da5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104961606&amp;doi=10.1016%2fj.chemolab.2021.104309&amp;partnerID=40&amp;md5=f4c3a6a54a3a7c8a1d49ff40cdab3da5</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 1697439; CODEN: CILSE Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Datta R., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India; Das D., Intel, Bengaluru, 560103, India; Das S., Sprinklr India Pvt Ltd, Yemalur, Bengaluru, 560037, India