# AI drug design and AlphaFold applications

The signal AlphaFold reads out of sequence alignments, what a predicted structure is and is not, and the energy-accuracy barrier that keeps computational design from replacing the assay.

Why coevolution in a protein family predicts a fold — and why a correct fold still does not give you a binding affinity.

Source: https://en.bioecon.ru/docs/health-biomedicine/therapeutics-platforms/ai-drug-design-alphafold-applications/
Updated: 2026-09-04



The idea underneath structure prediction is older than the machine learning that finally exploited it. If two residues touch in a folded protein, a mutation at one that destabilises the contact is tolerated only when a compensating mutation appears at the other. Over hundreds of millions of years and thousands of homologues, that constraint leaves a statistical trace: pairs of columns in a multiple sequence alignment that vary together. Read enough homologues and the contact map — and with it, roughly, the fold — is recoverable from sequence alone.

## What AlphaFold actually does with that signal

AlphaFold2's advance was not discovering coevolution but refusing to treat it as a separate step. Its Evoformer passes information back and forth between the alignment representation and a pairwise residue-residue representation, then a structure module places atoms directly in 3D and the whole network is recycled through its own output. At CASP14 in 2020 the median backbone accuracy crossed roughly 1 Å, comparable to experimental error for many targets, and the field's forty-year benchmark problem stopped being open. AlphaFold 3 extended the same architecture, with a diffusion-based generator, to complexes containing nucleic acids, ions and small molecules.

Two properties limit what follows. The model emits a per-residue confidence, pLDDT, and it is well calibrated: low pLDDT usually means the region is genuinely disordered rather than badly modelled — a useful answer, but not a structure. And the output is a single static conformation, biased toward whatever state the alignment's homologues were crystallised in. Proteins that matter pharmacologically are usually the ones that move: allosteric switches, cryptic pockets, active/inactive kinase states. A prediction that names one of those states does not tell you which one the drug should see.

## The energy barrier

The harder constraint is thermodynamic. Binding affinity depends exponentially on free energy, so at body temperature an error of about 1.4 kcal/mol — a small fraction of a single hydrogen bond — moves a predicted affinity by an order of magnitude. Empirical docking scoring functions are not close to that accuracy and were never designed to be; they rank poses, they do not measure binding. Rigorous alchemical methods such as free energy perturbation do approach roughly 1 kcal/mol on congeneric series, but only with a correct starting pose, a well-sampled protein, and GPU-hours per compound. Structure prediction has made the geometry cheap and left the energetics expensive, which is why the practical gain is in prioritising which of a billion enumerated compounds to synthesise, not in eliminating synthesis.

## Where the ceiling really sits

Generative chemistry has a related boundary: a model trained on known molecules reproduces the biases of known molecules, and proposals must still be synthesisable in a finite number of steps. But the deeper limit is that clinical attrition is not a chemistry problem. Most candidates fail because the target turned out not to drive the disease, or because of toxicity that no structure of the intended target predicts. Better molecules against a wrong hypothesis fail faster, not less often. Regulators have said as much in outline — the FDA's discussion paper on AI/ML in drug development and the EMA's reflection paper both centre on model transparency and validation rather than on accepting in-silico evidence in place of experiment.