Practical applications of deep learning to impute heterogeneous drug discovery data
This article outlines practical applications of deep learning on drug discovery data. It introduces some of the research behind our…
Practical applications of deep learning to imputation of drug discovery data
In this webinar, we explore how the limitations of pharmaceutical data can impact conventional predictive model building. Our speakers, Julian Levell (Constellation pharmaceuticals), Ben Irwin and matt Segall (Optibrium) demonstrate how the deep learning imputation algorithm underlying our Cerella platform, overcomes these challenges.
Predicting pKa using a combination of quantum mechanical and machine learning methods
This study aimed to create a model for predicting pKa using a semi-empirical quantum mechanics (QM) approach combined with machine learning (ML).
pKa prediction using quantum mechanics and machine learning
The dissociation of a proton from a heteroatom has a significant influence on the charge distribution and interactions of a…
N- and S-oxidation model of the flavin-containing monooxygenases
Introduction Existing computational models of drug metabolism are heavily focused on predicting oxidation by cytochrome P450 (CYP) enzymes, because of…
Imputation of assay bioactivity data using deep learning
This article describes a novel deep learning neural network method and its application for the imputation of bioactivity data, such…
WhichP450: a multi-class categorical model
This paper describes the underlying methods and validation of the WhichP450 model, which predicts the most likely Cytochrome P450 isoforms…
Predicting regioselectivity and lability of P450 metabolism
This article describes the underlying methods, validation and example applications of the most recent models of Cytochrome P450 metabolism in…
Addressing toxicity risk when designing and selecting compounds in early drug discovery
Summary In this article, ‘Addressing toxicity risk when designing and selecting compounds in early drug discovery‘, we discuss the application…
Human volume of distribution models
The volume of distribution (VDss) is an in vivo pharmacokinetic parameter representing the hypothetical volume into which the dose of drug would…
Shen HIA models
Access Shen HIA Models for robust predictions of human intestinal absorption. Improve drug development. Download now for enhanced bioavailability insights.
Shen BBB models
Blood-brain barrier (BBB) penetration is a measure of the ratio between the compound concentration in brain and blood. Good BBB…
Quantitative estimate of Drug-likeness in StarDrop
In the article, Bickerton et al. (2012) “The Chemical Beauty of Drugs” Nature Chemistry 4, 90–98, the authors proposed a measure of ‘drug-likeness’, the Quantitative…
HLM stability models
StarDrop™ HLM Stability Models are courtesy of Alexey Zakharov of the National Cancer Institute, National Institutes of Health, who has developed models of stability in Human liver Microsomes (HLM)
P-gp Transporter models
P-glycoprotein (P-gp) is an ATP driven efflux pump encoded by the MDR1 gene, capable of transporting a wide spectrum of…
Caco-2 Model
Accelerate drug development with Caco-2 Model. Predict intestinal permeability for better absorption. Download now for improved pharmacokinetic predictions.
Number of aromatic rings
Improve compound characterisation with StarDrop’s Number of Aromatic Rings tool. Download now for precise ring counting and structural analysis
Number of carbons
These two models calculates the number of sp3 carbons and the total number of carbons compound. These are available to…
Gaussian processes for classification: QSAR modeling of ADMET and target activity
Summary In this study, the researchers look to solve classification quantitative structure−activity relationship (QSAR) modelling problems using Gaussian processes. They…