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Interpretable machine learning methods for in vitro pharmaceutical formulation development

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Interpretation of QSAR Models Based on Random Forest Methods

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Biomacromolecular quantitative structure–activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein–protein binding affinity

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Deep learning for in vitro prediction of pharmaceutical formulations

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Predicting complexation performance between cyclodextrins and guest molecules by integrated machine learning and molecular modeling techniques

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A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation

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Descriptors and their selection methods in QSAR analysis: paradigm for drug design

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Combining molecular docking and QSAR studies for modelling the antigyrase activity of cyclothialidine derivatives

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Combining QSAR classification models for predictive modeling of human monoamine oxidase inhibitors

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In silico modelling of permeation enhancement potency in Caco-2 monolayers based on molecular descriptors and random forest

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Application of interpretable artificial neural networks to early monoclonal antibodies development

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QSAR based docking studies of marine algal anticancer compounds as inhibitors of protein kinase B (PKBβ)

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Stability assessment of extracts obtained from Arbutus unedo L. fruits in powder and solution systems using machine-learning methodologies

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In-silico screening of database for finding potential sweet molecules: A combined data and structure based modeling approach

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Near infrared spectroscopy for rapid and in-line detection of particle size distribution variability in lactose during mixing

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The utility of cyclodextrins for enhancing oral bioavailability

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Predicting physical stability of solid dispersions by machine learning techniques

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Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets

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DeepChemStable: Chemical Stability Prediction with an Attention-Based Graph Convolution Network

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Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?

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Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism

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Integrating in Silico and in Vitro Approaches To Predict Drug Accessibility to the Central Nervous System

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Computer-Aided Formulation Design for a Highly Soluble Lutein–Cyclodextrin Multiple-Component Delivery System

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An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction

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Quantitative Structure−Activity Relationship Studies on Inhibition of HERG Potassium Channels

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Introducing the Consensus Modeling Concept in Genetic Algorithms: Application to Interpretable Discriminant Analysis

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DrugLogit: Logistic Discrimination between Drugs and Nondrugs Including Disease-Specificity by Assigning Probabilities Based on Molecular Properties

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Harvesting Classification Trees for Drug Discovery

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Pre-processing Feature Selection for Improved C&RT Models for Oral Absorption

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Contemporary QSAR Classifiers Compared

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Accurate and Interpretable Computational Modeling of Chemical Mutagenicity

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Using Molecular Docking, 3D-QSAR, and Cluster Analysis for Screening Structurally Diverse Data Sets of Pharmacological Interest

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A Naive Bayes Classifier for Prediction of Multidrug Resistance Reversal Activity on the Basis of Atom Typing

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High-Throughput Screening of Drug−Brain Tissue Binding and in Silico Prediction for Assessment of Central Nervous System Drug Delivery

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Poorly Soluble Marketed Drugs Display Solvation Limited Solubility

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Accurate Models for P-gp Drug Recognition Induced from a Cancer Cell Line Cytotoxicity Screen

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Generation of a Set of Simple, Interpretable ADMET Rules of Thumb

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Chemical Fragments as Foundations for Understanding Target Space and Activity Prediction

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Molecular Properties Determining Unbound Intracellular and Extracellular Brain Exposure of CNS Drug Candidates

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Deep learning

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Going from where to why—interpretable prediction of protein subcellular localization

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Statistical interpretation of machine learning-based feature importance scores for biomarker discovery

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DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks

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YLoc—an interpretable web server for predicting subcellular localization

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Discovering protein–DNA binding sequence patterns using association rule mining

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Deep Residual Learning for Image Recognition

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