Machine learning (or, for investors, artificial intelligence) has received plenty of attention. To be successful you need lots of data. If you’re trying to, say, train a large language model to write limericks you’ve got oodles of text to draw from. But to train a model that can predict binding affinities you need lots of measurements, and they must be accurate. Among public repositories, ChEMBL is one of the most prominent due to its size (>2.4 million compounds) and quality, achieved through manual curation. But even here you need to be cautious, as illustrated in a recent open-access J ..read more

Practical Fragments has written frequently about hot spots, regions on proteins that are predisposed to bind ligands such as drugs. Determining whether a protein has a hot spot can help prioritize a target for screening, and one of the more established computational approaches to do so is FTMap, which we wrote about most recently just a couple months ago.   While FTMap can tell you whether a protein has one or more hot spots, it provides few further details, such as which regions might prefer a hydrogen bond donor or acceptor. This has now been addressed in a new J. Chem. Inf. Mod. paper ..read more

Last week the CHI Drug Discovery Chemistry (DDC) meeting was held in San Diego. This was the largest ever, with more than 900 participants, 95% of whom attended in person, up from 87% last year. I won’t attempt to cover all fourteen tracks but will just touch on some of the main themes.   Computational approaches All four days of the conference featured dedicated sessions on machine learning and artificial intelligence, but since I was in other sessions I don’t know how relevant they were to FBLD. If you attended an interesting talk please let me know so I can watch it on-demand.   A ..read more

Long-time readers of Practical Fragments will be familiar with various metrics for measuring molecules, such as LE, LLE, and WTF. But these are all hard-edged, numerical constructs. Some folks argue that we should take a softer, more nuanced approach. This call has been heeded by Katharine Bigg and Isabel Myerrors in the form of a “Myerrors-Bigg” Type Indicator, or MBTI.   The MBTI consists of a series of questions which rank a molecule into four dimensions: Extroversion/Introversion, Sociable/Nonsociable, Flat/Three-dimensional, and Pretty/Janky. Defining molecules as extroverts may soun ..read more

Rapidly proliferating cancer cells require a steady supply of nucleic acids, and cutting that off is a potential therapy. The enzyme dihydroorotate dehydrogenase (DHODH), which is important for pyrimidine synthesis, is thus an interesting drug target. In a recent ACS Med. Chem. Lett. paper, Lindsey DeRatt, Scott Kuduk, and colleagues at Janssen describe their approach.   The researchers had previously used virtual screening and structure-based drug design to develop compound 1, which is potent in both biochemical and cell-based assays. However, the molecule is highly effluxed by P-glycopr ..read more

One of the success stories we highlighted in last week’s summary of Fragments 2024 was the discovery of a potent inhibitor of SH2 domain-containing protein tyrosine phosphatase 2 (SHP2). James Day and colleagues at Astex and Taiho have just published the full account in J. Med. Chem.   Previous studies had shown that blocking SHP2 might be effective in certain cancers, particularly those dependent on mutant KRAS. As its name suggests, however, SHP2 is a phosphatase. This class of enzymes has highly charged active sites, which makes drug discovery notoriously difficult (see here for exampl ..read more

Last week saw the first of four dedicated fragment meetings this year: Fragments 2024, the 9th RSC-BMCS Fragment-based Drug Discovery Meeting, was held in historic Hinxton Hall, Cambridge, UK. I won’t attempt to cover the 17 talks, 40+ posters, and 20 exhibitors in detail but just try to hit on some broad themes.   One highlight was a talk by Chris Swain, whose Cambridge MedChem Consulting has come up several times at Practical Fragments. Chris has been systematically cataloging fragment hits reported in the literature, and his database now includes >2500 fragments from >300 papers ..read more

A well-curated fragment library is usually the starting point for fragment-based lead discovery, and not an insignificant investment. If you are just starting out you may want to use an existing library. One such option is described in an (open-access) paper in RSC Med. Chem. by Jordi Mestres and collaborators at IMIM Hospital del Mar Medical Research Institute and across Europe.   The EU-OPENSCREEN European Research Infrastructure Consortium (ERIC) allows researchers to access early lead discovery and chemistry resources. Among other components, it includes a set of more than 96,000 comp ..read more

It has been more than a year since our last list of fragment-derived clinical compounds. Since then capivasertib has been approved, bringing the number of marketed drugs to seven. There have also been a few other changes.   As always, this table includes compounds whether or not they are still in development (indeed, some of the companies no longer even exist). Because of this, the Phase 1 section contains a higher proportion of compounds that are no longer progressing. The full list contains 59 molecules, up slightly from 2022, with just under 40% approved or in active trials.   Dru ..read more

Practical Fragments has written several times about “hot spots”: regions on proteins where small molecules and fragments readily bind. Knowing whether your target protein has a hot spot can help you decide whether to pursue the target in the first place. A variety of computational approaches have been developed for finding hot spots, most of which start with a crystallographically determined structure. In a new J. Chem. Inf. Mod. paper, Sandor Vajda and collaborators at Boston University and Stony Brook University ask whether computational models of proteins can also be used for one of the mor ..read more