Deep Archetypal Analysis

"Deep Archetypal Analysis" generates latent representations of high-dimensional datasets in terms of fractions of intuitively understandable basic entities called archetypes. The proposed method is an extension of linear "Archetypal Analysis" (AA), an unsupervised method to represent multivariate data points as sparse convex combinations of extremal elements of the dataset. Unlike the original formulation of AA, "Deep AA" can also handle side information and provides the ability for data-driven representation learning which reduces the dependence on expert knowledge. Our method is motivated by studies of evolutionary trade-offs in biology where archetypes are species highly adapted to a single task. Along these lines, we demonstrate that "Deep AA" also lends itself to the supervised exploration of chemical space, marking a distinct starting point for de novo molecular design. In the unsupervised setting we show how "Deep AA" is used on CelebA to identify archetypal faces. These can then be superimposed in order to generate new faces which inherit dominant traits of the archetypes they are based on.Comment: Published at the German Conference on Pattern Recognition 2019 (GCPR

BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry

The increasing volume of biomedical data in chemistry and life sciences requires the development of new methods and approaches for their handling. Here, we briefly discuss some challenges and opportunities of this fast growing area of research with a focus on those to be addressed within the BIGCHEM project. The article starts with a brief description of some available resources for “Big Data” in chemistry and a discussion of the importance of data quality. We then discuss challenges with visualization of millions of compounds by combining chemical and biological data, the expectations from mining the “Big Data” using advanced machine-learning methods, and their applications in polypharmacology prediction and target de-convolution in phenotypic screening. We show that the efficient exploration of billions of molecules requires the development of smart strategies. We also address the issue of secure information sharing without disclosing chemical structures, which is critical to enable bi-party or multi-party data sharing. Data sharing is important in the context of the recent trend of “open innovation” in pharmaceutical industry, which has led to not only more information sharing among academics and pharma industries but also the so-called “precompetitive” collaboration between pharma companies. At the end we highlight the importance of education in “Big Data” for further progress of this area

Machine Learning, Quantum Mechanics, and Chemical Compound Space

We review recent studies dealing with the generation of machine learning models of molecular and solid properties. The models are trained and validated using standard quantum chemistry results obtained for organic molecules and materials selected from chemical space at random

Evaluating New Chemistry to Drive Molecular Discovery: Fit for Purpose?

As our understanding of the impact of specific molecular properties on applications in discovery-based disciplines improves, the extent to which published synthetic methods meet (or do not meet) desirable criteria is ever clearer. Herein, we show how the application of simple (and in many cases freely available) computational tools can be used to develop a semiquantitative understanding of the potential of new methods to support molecular discovery. This analysis can, among other things, inform the design of improved substrate scoping studies; direct the prioritization of specific exemplar structures for synthesis; and substantiate claims of potential future applications for new methods

Anthropogenic reaction parameters - the missing link between chemical intuition and the available chemical space

How do skilled synthetic chemists develop such a good intuitive expertise ? Why can we only access such a small amount of the available chemical space — both in terms of the re actions used and the chemical scaffolds we make? We argue here that these seemingly unrelated questions have a common root and are strongly interdependent . We performed a comprehensive analysis of organic reaction parameters dating back to 1771 and discove red that there are several anthropogenic factors that limit the reaction parameters and thus the scop e of synthetic chemistry. Nevertheless, many of the anthropogenic limitations such as the narrow parameter space and the opportunity of the rapid and clear feedback on the progress of reactions appear to be crucial for the acquisition of valid and reliable chemical intuition. In parallel, however, all of these same factors represent limitations for the exploration of available chemistry space and we argue th at these are thus at least partly responsible for limited access to new chemistries. We advocate, therefore, that the present anthropogenic boundaries can be expanded by a more conscious expl oration of “off - road” chemistry that would also extend the intuit ive knowledge of trained chemists

Evaluierung neuer Reaktionen zur Steuerung der Wirkstoff‐Forschung: ein Eignungstest

Mit der Verbesserung unserer Kenntnisse zur Bedeutung bestimmter Moleküleigenschaften bei Anwendungen im Rahmen der Wirkstoffsuche wird das Ausmaß immer klarer, in dem veröffentlichte Synthesemethoden erwünschten Kriterien genügen (oder nicht genügen). Wir beschreiben hier, wie die Anwendung einfacher (und in vielen Fällen frei verfügbarer) Rechenprogramme genutzt werden kann, um ein semiquantitatives Verständnis des Potenzials von neuen Methoden in der Wirkstoff‐Forschung zu entwickeln. Diese Analyse kann sich unter anderem auf die Planung verbesserter Studien zum Substratspektrum auswirken, die Priorisierung von bestimmten Beispielstrukturen für die Synthese leiten und Ansprüche für potenzielle künftige Anwendungen von neuen Methoden begründen