Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two
decades to become a successful key technology in the pharmaceutical industry for early …

In de novo drug design, computational strategies are used to generate novel molecules with
good affinity to the desired biological target. In this work, we show that recurrent neural …

There has been a recent surge of interest in using machine learning across chemical space
in order to predict properties of molecules or design molecules and materials with the …

Abstract Background The Chemistry Development Kit (CDK) is a widely used open source
cheminformatics toolkit, providing data structures to represent chemical concepts along with …

Cheminformatics has established itself as a core discipline within large scale drug discovery
operations. It would be impossible to handle the amount of data generated today in a small …

In this work, a machine learning (ML)-approach was developed to predict pure-component
parameters for the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) for non …

Designing a new drug is a lengthy and expensive process. As the space of potential
molecules is very large (Polishchuk, PG; Madzhidov, TI; Varnek, A. Estimation of the size of …

Enzymatic substrate promiscuity is more ubiquitous than previously thought, with significant
consequences for understanding metabolism and its application to biocatalysis. This …

Molecular target prediction can provide a starting point to understand the efficacy and side
effects of phenotypic screening hits. Unfortunately, the vast majority of in silico target …

Ligand-based models can be used in drug discovery to obtain an early indication of
potential off-target interactions that could be linked to adverse effects. Another application is …