GPCRs are the most commercially important receptor family and among the least forgiving to express. Expression level changes apparent pharmacology, the chosen readout determines which pathway you see, and the host cell background contributes signalling components you may not have accounted for. We design the construct, recommend the readout that matches the biology you care about, and model the receptor where structural insight informs mutagenesis or ligand work.
This is a computational (in silico) service. Designs and predictions prioritise hypotheses and direct experimental effort; they do not by themselves establish that an edit, construct, molecule or assay will perform as intended. Laboratory execution — editing, cloning, expression, synthesis and testing — is planned with you and carried out by your own team or a qualified partner. Deliverables are for research use only and are not intended for diagnostic or therapeutic use.
These are the failure modes that produce confident-looking results which do not survive the next experiment. Each one is designed against rather than discovered afterwards.
Illustrative of the shape this work takes. Details are generalised, no client or target is identified, and scope and outcome vary with the system in question.
The results below were published by independent groups and are cited to show what these computational approaches have achieved in the literature. They are not ICDMO project results, and outcomes vary substantially between targets, datasets and programmes. We do not promise comparable results for any particular project.
Current deep-learning structure prediction, evaluated on an independent protein–ligand benchmark, placed roughly 76% of ligands within 2 Å of the experimental pose, and reported approximately 80% by coordinate error — a substantial improvement over classical docking baselines on the same set, achieved without supplying a binding site. The corollary matters as much as the headline: roughly a quarter of cases still fall outside that threshold, which is why we report confidence per interface rather than delivering a single model.
Abramson et al., Nature, 2024 ↗Note: ICDMO delivers computational (in silico) services. Results prioritise hypotheses for testing; they do not by themselves establish binding, biological activity, efficacy or safety. All deliverables are for research use only and are not intended for diagnostic or therapeutic use. Downstream experimental work is scoped separately and executed through qualified partners.
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