Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

Olier, Iván; Sadawi, Noureddin; Bickerton, G. Richard J.; Vanschoren, Joaquin; Groşan, Crina; Soldatova, Larisa; King, Ross D.

doi:10.1007/s10994-017-5685-x

Cited by 73 publications

(63 citation statements)

References 57 publications

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“…More generally, determining which method performs better on which problem is a non-trivial problem and is the subject of meta-learning (e.g. Olier et al (2018)).…”

Section: Discussionmentioning

confidence: 99%

An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat

2019

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In phenotype prediction the physical characteristics of an organism are predicted from knowledge of its genotype and environment. Such studies, often called genome-wide association studies, are of the highest societal importance, as they are of central importance to medicine, crop-breeding, etc. We investigated three phenotype prediction problems: one simple and clean (yeast), and the other two complex and real-world (rice and wheat). We compared standard machine learning methods; elastic net, ridge regression, lasso regression, random forest, gradient boosting machines (GBM), and support vector machines (SVM), with two stateof-the-art classical statistical genetics methods; genomic BLUP and a two-step sequential method based on linear regression. Additionally, using the clean yeast data, we investigated how performance varied with the complexity of the biological mechanism, the amount of observational noise, the number of examples, the amount of missing data, and the use of different data representations. We found that for almost all the phenotypes considered, standard machine learning methods outperformed the methods from classical statistical genetics. On the yeast problem, the most successful method was GBM, followed by lasso regression, and the two statistical genetics methods; with greater mechanistic complexity GBM was best, while in simpler cases lasso was superior. In the wheat and rice studies the best two methods were SVM and BLUP. The most robust method in the presence of noise, missing data, etc. was random forests. The classical statistical genetics method of genomic BLUP was found to perform well on problems where there was population structure. This suggests that standard machine learning methods need to be refined to include population structure information when this is present. We conclude that the application of machine learning methods to phenotype prediction problems holds great promise, but that determining which methods is likely to perform well on any given problem is elusive and non-trivial.

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“…More generally, determining which method performs better on which problem is a non-trivial problem and is the subject of meta-learning (e.g. Olier et al (2018)).…”

Section: Discussionmentioning

confidence: 99%

An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat

2019

Self Cite

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“…It is important to note too that PM decoys are not required either to train or test QSAR models [35], despite predicting exactly the same in vitro potency/affinity endpoints as SFs (e.g. K d is predicted by both SFs [36,37] and QSAR models [38,39]).…”

Section: Selecting a Scoring Function Based On Your Own Evaluationmentioning

confidence: 99%

Selecting machine-learning scoring functions for structure-based virtual screening

Ballester

2019

Drug Discovery Today: Technologies

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Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at leveraging these experimental structures to discover the necessary starting points for the drug discovery process. It is now established that Machine Learning (ML) can strongly enhance the predictive accuracy of scoring functions for SBVS by exploiting large datasets from targets, molecules and their associations. However, with greater choice, the question of which ML-based scoring function is the most suitable for prospective use on a given target has gained importance. Here we analyse two approaches to select an existing scoring function for the target along with a third approach consisting in generating a scoring function tailored to the target. These analyses required discussing the limitations of popular SBVS benchmarks, the alternatives to benchmark scoring functions for SBVS and how to generate them or use them using freely-available software.

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“…The problem of building models to predict drug effects has been widely studied, from potential adverse effects [33] and drug-drug interactions [32] to cancer cell sensitivity [23]. One such task is the learning of quantitative structure activity relationships (QSARs), where one is interested in predicting the effect of a drug or chemical compound from its molecular structure [25]. Molecular structure is usually represented using molecular fingerprints, which are tuples of Boolean descriptors [5].…”

Section: Related Workmentioning

confidence: 99%

Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology

Orhobor

French²,

Soldatova

et al. 2020

Lecture Notes in Computer Science

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The key to success in machine learning is the use of effective data representations. The success of deep neural networks (DNNs) is based on their ability to utilize multiple neural network layers, and big data, to learn how to convert simple input representations into richer internal representations that are effective for learning. However, these internal representations are sub-symbolic and difficult to explain. In many scientific problems explainable models are required, and the input data is semantically complex and unsuitable for DNNs. This is true in the fundamental problem of understanding the mechanism of cancer drugs, which requires complex background knowledge about the functions of genes/proteins, their cells, and the molecular structure of the drugs. This background knowledge cannot be compactly expressed propositionally, and requires at least the expressive power of Datalog. Here we demonstrate the use of relational learning to generate new data descriptors in such semantically complex background knowledge. These new descriptors are effective: adding them to standard propositional learning methods significantly improves prediction accuracy. They are also explainable, and add to our understanding of cancer. Our approach can readily be expanded to include other complex forms of background knowledge, and combines the generality of relational learning with the efficiency of standard propositional learning.

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Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

Cited by 73 publications

References 57 publications

An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat

An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat

Selecting machine-learning scoring functions for structure-based virtual screening

Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology

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