Deep Learning in Chemistry

Deep learning (DL) is all the rage these days and this approach to predictive modeling is being applied to a wide variety of problems, including many in computational drug discovery. As a dilettante in the area of deep learning, I’ve been following papers that have used DL for cheminformatics problems, and thought I’d mention a […]

From Algorithmic Fairness to QSAR Models

The topic of algorithmic fairness has started recieving a lot of attention due to the ability of predictive models to make decisions that might discriminate against certain classes of people. The reasons for this include biased training data, correlated descriptors, black box modeling methods or a combination of all three. Research into algorithmic fairness attempts […]

Which Datasets Lead to Predictive Models?

I came across a recent paper from the Tropsha group that discusses the issue of modelability – that is, can a dataset (represented as a set of computed descriptors and an experimental endpoint) be reliably modeled. Obviously the definition of reliable is key here and the authors focus on a cross-validated classification accuracy as the […]

Predictive models – Implementation vs Specification

Benjamin Good recently asked about the existence of public repositories of predictive molecular signatures. From his description, he’s looking for platforms that are capable of deploying predictive models. The need for something like this is certainly not restricted to genomics – the QSAR field has been in need for this for many years. A few […]

I’d Rather Be … Reverse Engineering

Gamification is a hot topic and companies such as Tunedit and Kaggle are succesfully hosting a variety of data mining competitions. These competitions employ data from a variety of domains such as bond trading, essay scoring and so on. Recently, both platforms have hosted a QSAR challenge (though not officially denoted as such). The most […]