How much does 3D molecular modelling software cost?
Introduction 3D molecular modelling plays a vital role in modern drug discovery, offering powerful applications to streamline research, reduce costs,…
How can categorical models provide value in supporting compound prioritisation?
Introduction In early-stage drug discovery, medicinal chemists rely on predictive models to help guide which compounds to synthesise or test…
How to evaluate the performance of QSAR/QSPR classification models?
Introduction After training a classification model, we would like to evaluate its performance by using the trained model on an…
Derek Nexus for toxicity prediction – What package is right for me?
What is Derek Nexus? Developed by Lhasa Limited, Derek Nexus is an expert-knowledge based system that draws on over 40…
Which ADMET properties are important for me to predict?
How can I predict my compound’s absorption? The first of the ADMET properties relate to absorption. Understanding how a drug…
Machine Learning 101: How to train your first QSAR model
My hope is that these posts will be of interest to people who want to understand more of the nuts…
How does StarDrop compare to Semeta?
What are StarDrop and Semeta? Semeta is a tailored platform for DMPK scientists. It enables users to address key challenges…
How do I know if Optibrium’s predictive models work?
Data curation for model building A model can only be as good as the data it has been trained on.…
How can I make the most of my predictive models for drug discovery?
What’s the purpose of a predictive model? What’s the value of predictive models for drug discovery? Most of the undergraduate…
What’s the difference between QSAR and imputation predictive models – which method should I use and when?
We’re often asked, “What’s the difference between QSAR and imputation models?”, so I’m going to explain how the methods differ, their advantages and disadvantages, and when each approach is applicable.
Perfecting the use of imperfect QSAR models
In this webinar, we examine the effective use of QSAR modelling in drug discovery and discuss a variety of pain points for medicinal chemists in knowing when a model can be trusted and how to avoid common pitfalls.
Automatic generation of new compound ideas
During this example we will consider three compounds from a lead series which we would like to try to evolve into a candidate. The compound has a good profile of ADME properties but insufficient inhibition of the target, the Serotonin transporter. In this example we will use StarDrop’s Nova module to generate new ideas for compounds to improve the potency while maintaining the balance of other properties.
Imputation of sensory properties using deep learning: webinar
In this webinar, we demonstrate how Augmented Chemistry®, a unique deep learning method, can learn from higher throughput data together with limited panel data to provide high-quality imputations for sensory properties.
eSim3D: electrostatic-field and surface-shape similarity for ligand-based drug design
In this webinar, we present eSim3D, a novel ligand-based drug design approach based on electrostatic-field and surface-shape similarity coupled with unique conformational search capabilities, offering unprecedented accuracy and performance.
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…
