Virtual screening is a commonly used strategy to search for promising molecules among millions of existing or billions of virtual molecules. Given this practical limitation of in vitro experiments, it is desirable to have computational tools to narrow down the enormous search space.

(4) However, larger experiments will get prohibitively expensive.

Modern high-throughput screening techniques allow testing of molecules on the order of 10 6 in the lab. Active means for example that a molecule binds to a biomolecule, which causes an effect in the living organism, or inhibits replication of bacteria. (3) Chemists have to select and examine molecules from this large space to find molecules that are active toward a biological target. It has been estimated that 10 60 drug-like molecules could possibly be synthetically accessible. (2) One of the many challenges in drug design is the sheer size of the search space for novel molecules. However, creating novel drugs is an extraordinarily hard and complex problem. (1) This is particularly true for medicinal chemistry. Chemists speak it fluently and have made their discipline one of the true contributors to human well-being, which has “change the way you live and die”. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery.Ĭhemistry is the language of nature. Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria), it reproduced 28% of 1240 test molecules. In order to enrich libraries with molecules active toward a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model.

In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target.

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