Data Mining of Chemogenomics Data Using Bi‐Modal PLS Methods and Chemical Interpretation for Molecular Design

Hasegawa, Kiyoshi; Funatsu, Kimito

doi:10.1002/minf.201400061

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…Other PLS methods, like orthogonal PLS (OPLS), O2PLS, L-shaped PLS, and orthogonal LPLS have also found application in chemoinformatics. They can be interpreted similarly to conventional PLS models on the basis of regression coefficients, scores, and loading. , …”

Section: Machine Learning Method-specific Interpretation Approachesmentioning

confidence: 99%

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Interpretation of Quantitative Structure–Activity Relationship Models: Past, Present, and Future

Polishchuk

2017

J. Chem. Inf. Model.

191

146

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This paper is an overview of the most significant and impactful interpretation approaches of quantitative structure-activity relationship (QSAR) models, their development, and application. The evolution of the interpretation paradigm from "model → descriptors → (structure)" to "model → structure" is indicated. The latter makes all models interpretable regardless of machine learning methods or descriptors used for modeling. This opens wide prospects for application of corresponding interpretation approaches to retrieve structure-property relationships captured by any models. Issues of separate approaches are discussed as well as general issues and prospects of QSAR model interpretation.

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Section: Machine Learning Method-specific Interpretation Approachesmentioning

confidence: 99%

“…They can be interpreted similarly to conventional PLS models on the basis of regression coefficients, scores, and loading. 30,31 Decision Trees. The predictive ability of linear models is limited when the linear trend of structure−property relation-ships cannot be established.…”

Section: = * T Xwmentioning

confidence: 99%

Interpretation of Quantitative Structure–Activity Relationship Models: Past, Present, and Future

Polishchuk

2017

J. Chem. Inf. Model.

191

146

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“…Hence, the availability of cheminformatic tools that are easy to use can help reduce time and cost in this complex drug discovery field [107]. Similarly, various connections between protein and ligands can also be established using these widely available resources [108]. The presence of a large number of experimental and biological databases containing relevant screened compounds is easily accessible via a public domain.…”

Section: Chemical and Biological Data Repositoriesmentioning

confidence: 99%

Towards reproducible computational drug discovery

et al. 2020

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The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.

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