Machine learning methods in chemoinformatics

By John B. O. Mitchell. 2014

412 citations recorded by the source corpus.

Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure–activity relationships (QSAR), many others exist in the technical literature. This discussion is methods‐based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k‐Nearest Neighbors and naïve Bayes classifiers. WIREs Comput Mol Sci 2014, 4:468–481.

Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy

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Citations in the corpus

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks (2017, methodology): “14,15 The molecules are split into actives and inactives using a threshold on a measure for the substance e ﬀ ectiveness. pIC 50 = − log 10 (IC 50 ) is one of the most widely used metrics for this purpose.”
Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning (2019, positive): “The power of these methods is particularly notable for catalyst design; modern machine learning methods have the capacity to find patterns in large sets of data that are incomprehensible to experimental practitioners (17).”
QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery (2018, background): “…modeling has grown, diversiﬁed, and evolved to the modeling and virtual screening (VS) of very large data sets comprising thousands of diverse chemical structures and using a wide variety of machine learning techniques (Cherkasov et al., 2014; Mitchell, 2014; Ekins et al., 2015; Goh et al., 2017).”
SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules (2017, positive): “Remarkably, computer-aided drug design (CADD) has been a pioneer field for the application of such machine learning technologies 13 .”
Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns (2019, methodology): “Artiﬁcial Intelligence (AI), and machine learning (ML) in particular, are committed to recognizing and learn from these patterns (Mitchell, 2014; Rupp, 2015; Goh et al., 2017; Li et al., 2017; Butler et al., 2018; Fleming, 2018; Gao et al., 2018; Kishimoto et al., 2018; Popova et al., 2018;…”
Machine learning: Accelerating materials development for energy storage and conversion (2020, methodology): “The selection of an appropriate ML model also plays a key role since it significantly affects the predicted performance.(57) There is no single best method for all cases.”
The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology (2016, background): “Quantitative structure–activity relationships (QSAR) and quantitative structure–property relationships (QSPR) are still considered as the modern standard for predicting properties of novel molecules [30].”
Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets. (2017, methodology): “…approach which we propose is to use machine learning which can deal with this big data used in cheminformatics with methods such as support vector machines (SVM) 5–11 , k-Nearest Neighbors (kNN) 12 , Naïve Bayesian 13–17 , Decision Trees 18 and others 19 which have been increasingly used 4, 20–22 .”