The goal of hit discovery is to identify suitable chemical starting points to modulate a drug target. A hit can be, a.o., a small molecule, a protein or mRNA. Hit identification is performed via rational design, genome mining, (targeted) library screening, or in silico approaches.

Hit discovery from computer to molecule synthesis

Researchers within the LED3 Network have vast expertise in the synthesis, isolation, and study of bioactive molecules. This experience covers a broad chemical space including drug-like small molecules, natural products, peptides, complex carbohydrates, and lipids.

For example, we can use these methods to design and synthesize small molecule inhibitors against enzymes that are known or suspected drivers of diseases such as cancer, metabolic disorder and inflammation. Another line of research is the identification of novel macrocyclic peptides to block targets in infectious disease.

In silico discovery

We have the possibility to apply artificial intelligence to aid us in computational mining. These AI-technologies can, for example, be used to prioritize biosynthetic gene clusters likely to be involved in the production of antibiotics and natural products with other relevant biological activities. We can use CRISPR-Cas knock-outs, heterologous expression and regulatory engineering to link these genes to their molecular products and isolate quantities sufficient for structural characterization. 
At the same time, untargeted metabolomics and spectral networking techniques can help pinpoint new, naturally occurring variants of metabolites or link active fractions to the presence of specific molecules.

We combine digital brains and power to find hits

Our expertise in the area of computational drug discovery is also present in AI-based machine learning approaches and structure-based methods that rely on 3D structural information. Additionally, new compounds are automatically generated using SMILES-based de novo generative algorithms that can optimize simultaneously for multiple – often contradictory – requirements that are part of drug discovery. 
At LED3, we can make use of the Computational Drug Discovery group’s in-house CPU and GPU clusters as well as the Leiden University high-performance cluster ALICE.

Smart synthesis and testing of hits

Using our expertise in medicinal chemistry, we are able to design compounds with an optimal balance in properties such as physicochemical profile and target affinity. Alternatively, we can also apply biosynthetic engineering to generate new analogues using enzymes, guided by natural variation observed from genome and metabolome data when available.

Molecular pharmacology is also part of this ‘Design-Make-Test’ cycle in drug discovery. In this cycle, newly designed and synthesized compounds are being evaluated for their affinity or potency, providing feedback to medicinal chemistry for further optimization. We use our chemistry labs with 100+ fume hoods to synthesize compounds and the Cell Observatory to generate bio-assays to study the activity of compounds of interest.