Extrapolative prediction using physically-based QSAR
Surflex-QMOD integrates chemical structure and activity data to produce physically-realistic models for binding affinity prediction.
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…
Preprint: considering the impact of ‘drug-like’ properties on the chance of success
Summary There are many different definitions of ‘drug-like’, with rules proposed based on property trends seen in successful drugs. In…
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…
Topological Polar Surface Area – Including S and P
In J. Med. Chem., 2000, 43 (20), pp 3714–3717, Ertl et al. propose the calculation of two polar surface area values, the first reports the PSA…
ADMET property prediction: The state of the art and current challenges
Summary This article discusses Quantitative Structure – Activity relationships (QSAR) methods to predict absorption, distribution, metabolism, excretion and toxicity (ADMET)…
Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility
Summary In this study, our researchers combined an automatic model generation process for building QSAR models with the Gaussian Processes…
Gaussian processes for classification
In this article, Olga describes how we extend the application of Gaussian Processes technique to classification problems. These computational techniques…
Beyond profiling: using ADMET models to guide decisions
Summary The main use of ADMET models, whether in silico or in vitro,tends to be molecule ‘profiling’; identifying compounds which are expected to…