Fr\'echet ChemNet Distance: A metric for generative models for molecules in drug discovery

The new wave of successful generative models in machine learning has increased the interest in deep learning driven de novo drug design. However, assessing the performance of such generative models is notoriously difficult. Metrics that are typically used to assess the performance of such generative models are the percentage of chemically valid molecules or the similarity to real molecules in terms of particular descriptors, such as the partition coefficient (logP) or druglikeness. However, method comparison is difficult because of the inconsistent use of evaluation metrics, the necessity for multiple metrics, and the fact that some of these measures can easily be tricked by simple rule-based systems. We propose a novel distance measure between two sets of molecules, called Fr\'echet ChemNet distance (FCD), that can be used as an evaluation metric for generative models. The FCD is similar to a recently established performance metric for comparing image generation methods, the Fr\'echet Inception Distance (FID). Whereas the FID uses one of the hidden layers of InceptionNet, the FCD utilizes the penultimate layer of a deep neural network called ChemNet, which was trained to predict drug activities. Thus, the FCD metric takes into account chemically and biologically relevant information about molecules, and also measures the diversity of the set via the distribution of generated molecules. The FCD's advantage over previous metrics is that it can detect if generated molecules are a) diverse and have similar b) chemical and c) biological properties as real molecules. We further provide an easy-to-use implementation that only requires the SMILES representation of the generated molecules as input to calculate the FCD. Implementations are available at: https://www.github.com/bioinf-jku/FCDComment: Implementations are available at: https://www.github.com/bioinf-jku/FC

A normalization technique for next generation sequencing experiments

Next generation sequencing (NGS) are these days one of the key technologies in biology. NGS' cost effectiveness and capability of finding the smallest variations in the genome makes them increasingly popular. For studies aiming at genome assembly, differences in read count statistics do not affect the outcome. However, these differences bias the outcome if the goal is to identify structural DNA characteristics like copy number variations (CNVs). Thus a normalization step must removed such random read count variations subsequently read counts from different experiments are comparable. Especially after normalization the commonly used assumption of Poisson read count distribution in windows on the chromosomes is more justified. Strong deviations of read counts from the estimated mean Poisson distribution indicate CNVs

Identifying Copy Number Variations based on Next Generation Sequencing Data by a Mixture of Poisson Model

Next generation sequencing (NGS) technologies have profoundly impacted biological research and are becoming more and more popular due to cost effectiveness and their speed. NGS can be utilized to identify DNA structural variants, namely copy number variations (CNVs) which showed association with diseases like HIV, diabetes II, or cancer.

There have been first approaches to detect CNVs in NGS data, where most of them detect a CNV by a significant difference of read counts within neighboring windows at the chromosome. However these methods suffer from systematical variations of the underlying read count distributions along the chromosome due to biological and technical noise. In contrast to these global methods, we locally model the read count distribution characteristics by a mixture of Poissons which allows to incorporate a linear dependence between copy numbers and read counts. Model selection is performed in a Bayesian framework by maximizing the posterior through an EM algorithm. We define a CNV call which indicates a deviation of the Poisson mixture parameters from the null hypothesis represented by the prior which is a model for constant copy number across the samples. A CNV call requires sufficient information in the data to push the model away from the null hypothesis given by the prior.

We test our approach on the HapMap cohort where we rediscovered previously found CNVs which validates our approach. It is then tested on the tumor genome data set where we are able to considerably increase the detection while reducing the false discoveries.
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