Using machine learning techniques for rationalising phenotypic readouts from a rat sleeping model
TL;DR: This work explores machine learning methods for rationalising phenotypic readouts from a rat model for hypnotics based on a polypharmacology approach and hypothesises that by combining target prediction and machine learning techniques the authors are able to derive information regarding the mode of action of small molecules.
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Abstract: Understanding the mode of action of small molecules is critical for drug research, both with respect to efficacy and anticipated side effects. Given that many compounds act on multiple targets simultaneously, it appears that linking single targets to outcomes is no longer sufficient. Hence, in this work we explore machine learning methods for rationalising phenotypic readouts from a rat model for hypnotics based on a polypharmacology approach. We hypothesise that by combining target prediction and machine learning techniques we are able to derive information regarding the mode of action of small molecules. In particular, we applied this hypothesis on a subset of the Eli Lilly SCORE™ dataset. This comprised 845 data instances, each consisting of 7 phenotypic readouts which attribute towards a good sleeping pattern. We employed a target prediction tool[1] to anticipate bioactivities of ligands in combination with the CN2 and C4.5 machine learning algorithms to derive interpretable rule lists and classification trees for the observed phenotypes. A review of the known mechanisms of action for the largest categories of hypnotics suggests that our results are in most cases consistent with current literature on the mode of action of hypnotics. This suggests that our method can potentially yield significant information regarding the mode of action of hypnotics, and in addition novel targets that are not yet well-established in literature. As further applications of this work, we are currently preparing to apply our methodology to a subset of a Traditional Chinese Medicine dataset and a phenotypic screening dataset for Xenopus.
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Citations
Target prediction utilising negative bioactivity data covering large chemical space
Lewis H. Mervin,Avid M. Afzal,Georgios Drakakis,Richard J. Lewis,Ola Engkvist,Andreas Bender +5 more
TL;DR: The inclusion of large scale negative training data for in silico target prediction improves the precision and recall AUC and BEDROC scores for target models and results from internal and external validation of the models show differing performance between the breadth of models.
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Machine Learning in Drug Design
Ola Engkvist,Lewis Mervin,Hongming Chen,Ting Ran +3 more
- 03 Feb 2023
TL;DR: Machine learning in drug design is a rapidly growing field that has been successful in predicting ADMET properties, designing new molecules, and planning synthesis and retrosynthesis.
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MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products
Paula Carracedo-Reboredo,Eider Aranzamendi,Shan He,Sonia Arrasate,Cristian R. Munteanu,Carlos Fernandez-Lozano,Nuria Sotomayor,Esther Lete,Humberto González-Díaz +8 more
TL;DR: Researchers developed MATEO, a web-based tool for optimizing enantioselective α-amidoalkylation reactions using Chiral Phosphoric Acid catalysts, achieving a predictive model with 96% accuracy through Heuristic Perturbation-Theory and Machine Learning.
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MATEO: InterMolecular α-Amidoalkylation Theoretical Enantioselectivity Optimization. Online Tool for Selection and Design of Chiral Catalysts and Products
03 Mar 2023
TL;DR: In this article , an HPTML algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R 2 = 0.91.
References
In Silico Target Predictions: Defining a Benchmarking Data Set and Comparison of Performance of the Multiclass Naïve Bayes and Parzen-Rosenblatt Window
Alexios Koutsoukas,Robert Lowe,Yasaman KalantarMotamedi,Hamse Y. Mussa,Werner Klaffke,John B. O. Mitchell,Robert C. Glen,Andreas Bender +7 more
TL;DR: Two probabilistic machine-learning algorithms were compared for in silico target prediction of bioactive molecules, namely the well-established Laplacian-modified Naïve Bayes classifier (NB) and the more recently introduced Parzen-Rosenblatt Window.