The primary hurdle in drug development is the Phase 2 trial, where the most frequent cause of failure is a simple lack of efficacy. It is not typically due to safety concerns, business case changes, or target engagement issues, but rather that the drug produces no therapeutic effect upon administration.
A critical disconnect exists in drug development: the decision to start a trial is most influenced by the number of academic publications on a target. However, this metric has no bearing on the trial's likelihood of success. The best predictor of success is actually strong human genetic evidence linking the target to the disease.
AI's initial pharma application focused on molecule design, driven by commercial incentives. New molecules are patentable and thus more easily fundable. This occurred despite incorrect target selection being the primary cause of drug development failure, representing a less immediately commercializable but more fundamental problem.
A critical distinction for target selection is whether a target influences a disease's ongoing progression or merely a person's susceptibility to it. Since most medicines are designed to treat patients who already have a condition, the target must be involved in the active progression of the illness to have a therapeutic effect.
Previously, genetic validation was a perfunctory, yes/no question asked late in the drug discovery process. Now, sophisticated VCs and pharma companies demand this evidence much earlier, recognizing its critical role in predicting clinical success. It has become a prerequisite for investment rather than a final confirmation step.