What was your biggest professional highlight of the past 12 months?
The continued advancement and uptake of my work with colleagues in the macrocyclic peptide space has been extremely gratifying. Our most recent joint work with Merck was just published in JCIM ("CSCAN: Conformational Analysis of Macrocyclic Peptides through NMR Chemical Shifts"). We show how to use our methods for deep conformational sampling in combination with NMR chemical shift data to identify biologically relevant conformations of complex macrocyclic peptides. This complements our prior work with the ForceGen methodology, demonstrating fast and effective purely computational search on small- to mid-sized macrocycles (e.g. med-chem designed molecules such as vaniprevir up to natural products such as rapamycin) and conformational search *augmented* with experimental NMR data on larger macrocycles (e.g. the cyclic 8-residue depsipeptide Aureobasidin up to very large 15-mer peptidic macrocycles such as those optimized for PD-L1 binding). Coupled with fast and accurate docking and similarity approaches (the Surflex-Dock and eSim methods), the work transforms optimization of diverse macrocycles into one that can be vastly accelerated using computationally tractable procedures.
Have small molecules been overshadowed by newer modalities – and where do they still hold a clear advantage?
Small molecules have clear advantages, including (relative) ease of optimization for oral dosing, tractability for computational modelling ranging from virtual screening to lead optimization spanning affinity/selectivity enhancement, and ADMET improvement. Rather than being overshadowed, it might be better to think of the small-molecule modality being expanded. Improvements in experimental methodology now commonly provide high-affinity macrocyclic peptide lead compounds using phage- and mRNA-display. Medicinal chemistry teams can now tackle complex macrocycles coming from such experimental screening approaches to design selective, potent, and orally available compounds such as Merck's bridged PCSK9 macrocyclic MK-0616 (Enlicitide). Computational methodology has enhanced our ability to model the behaviour of such compounds, especially in the context of biophysical data from NMR and X-ray crystallography or Cryo-EM. The small-molecule concept had largely conformed to the rule-of-five complexity/property limits, but it is now expanding beyond the RO5 space.
What’s one widely held belief in your field that you disagree with – and why?
There is a belief among many that the chemical matter required to address serious unmet therapeutic needs either already exists or is a minor structural modification from something that does. This assumption takes two forms. First, relying on drug repurposing as a strategy for drug discovery is perhaps too optimistic. This assumes that the cure for something like Alzheimer's disease, multiple sclerosis, or some form of cancer has already been made and has gone through regulatory approvals for some other disease. It is certainly true that nearly all drugs have multiple off-targets and that those are often quite different from the intended therapeutic targets. However, it seems extremely unlikely that an Alzheimer's cure is in a jar on a pharmacy shelf, and we just need to find the right jar.
Second, complex models that contain millions (or even billions) of parameters that have been trained on vast quantities of data, such as that in the PDB and ChEMBL, are very good at absorbing, indexing, and interpolating within the training corpus. Using such models can be valuable, but, as with drug repurposing, the challenges posed by serious unmet patient needs are likely to require structurally novel molecules that will require significant extrapolation beyond what has already been made and experimentally tested.
This is not to say that strategies that involve drug repurposing or mega-parameter AI models are not valuable. But drug discovery researchers should understand the assumptions that allow such approaches to be successful in some cases, and that those assumptions begin to break down in challenging and therapeutically important discovery scenarios.
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