Leiden Early Drug Discovery & Development
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.
Pathogenic bacteria are becoming more and more resistant to our antibiotics, and this poses a great challenge for future treatments. One particular promising source of new antibiotics is peptides. We recently achieved a breakthrough in using artificial intelligence to recognize generic patterns associated with the production of these peptides. This allows the systematic discovery of dozens of new classes of peptide natural products. Read more
Antimicrobial resistance (AMR) is the phenomenon that pathogens become insensitive to the antibiotics that we use against them. Fortunately, we have recently discovered a new class of antibiotics. These new antibiotics, potent semi-synthetic glycopeptides, exhibit exceptional activity against MRSA and a range of vancomycin-resistant organisms. Read more
Researchers at LED3 are working together with biopharmaceutical company Galapagos to develop software for use in early drug discovery. This software is able to design molecules with several simultaneously optimized characteristics and will also take prediction reliability into consideration in order to better manage the 'Design-Make-Test' cycle. Read more