What decision does “Go/No-Go Criteria for Early Drug Discovery” help make?

It supports a bounded decision about whether a target or program should enter the next discovery stage. The framework keeps alternatives, evidence, counterevidence, uncertainty, and the fastest falsification test visible.

Can computation validate the final scientific claim?

No. Passing an early gate does not predict eventual approval or efficacy. Computation can prioritize and eliminate directions; final validation remains with the appropriate domain methods and accountable specialists.

When should the project stop or reframe?

Stop or reframe when the target-disease link is not robust, the mechanism lacks a discriminating prediction, tractability depends on unsupported assumptions, public data contradict the thesis, or the next credible validation belongs in a qualified experimental program.

Drug Discovery

Go/No-Go Criteria for Early Drug Discovery

Published 2026-08-22 · Updated 2026-08-22

Answer in brief

Go/No-Go Criteria for Early Drug Discovery becomes decision-useful when the team states whether a target or program should enter the next discovery stage, not when it collects another undirected summary. Use predeclared biological, technical, safety, differentiation, and feasibility thresholds to compare mechanisms and run this early falsifier: require the top load-bearing claim to reproduce under an orthogonal method. Continue only if the ranking survives.

Evidence status: Decision-method guide; not a completed investigation or final validation.

The decision this guide supports

whether a target or program should enter the next discovery stage

Why the problem is difficult

The article-specific identification challenge is whether the question “whether a target or program should enter the next discovery stage” can be resolved using predeclared biological, technical, safety, differentiation, and feasibility thresholds, rather than merely restated in new language.

A falsifier-first workflow

Define the decision precisely: whether a target or program should enter the next discovery stage.
Build a source and data ledger around predeclared biological, technical, safety, differentiation, and feasibility thresholds.
Compare the inherited route with a mechanistically distinct alternative and a constraint-based null.
Actively search for the strongest counterevidence relevant to this decision, including boundary cases and prior failures.
Run the lowest-cost discriminating challenge: require the top load-bearing claim to reproduce under an orthogonal method.
Record pursue, reframe, or stop, the confidence level, the evidence ceiling, and who owns downstream validation.

Decision criteria

Decision impact: would the result materially change the choice about whether a target or program should enter the next discovery stage?
Evidence fit: does the available evidence—predeclared biological, technical, safety, differentiation, and feasibility thresholds—directly address the decision rather than merely correlate with it?
Discrimination: does the preferred route predict an outcome a credible alternative does not?
Robustness: does the ranking survive the challenge “require the top load-bearing claim to reproduce under an orthogonal method”?
Validation boundary: is the conclusion no stronger than the available sources, data and computation?

Supporting evidence

predeclared biological, technical, safety, differentiation, and feasibility thresholds

Counterevidence

For this decision, a result from “require the top load-bearing claim to reproduce under an orthogonal method” that reverses or flattens the ranking must remain visible even when it is commercially inconvenient.

Computation

Here computation earns its place only if it changes the choice about whether a target or program should enter the next discovery stage or exposes why the available evidence cannot resolve it.

Fastest falsifier

require the top load-bearing claim to reproduce under an orthogonal method

When to stop or reframe

A decision-specific stop trigger is failure of the challenge “require the top load-bearing claim to reproduce under an orthogonal method” without an independently supported alternative mechanism.

Evidence ceiling

Passing an early gate does not predict eventual approval or efficacy.

Sources and starting points

Open Targets Platform — Integrated public evidence connecting therapeutic targets and diseases.
ChEMBL — Curated bioactivity information for compounds, assays, and targets.
ClinicalTrials.gov — Official registry and results database for clinical studies; registry records are not proof of efficacy.
PubMed — Primary biomedical literature discovery; individual studies require direct appraisal.
AlphaFold Protein Structure Database — Additional authoritative starting point selected for this decision area; applicability must be checked against the precise question.
Europe PMC — Additional authoritative starting point selected for this decision area; applicability must be checked against the precise question.

Continue the decision journey

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Frequently asked questions

What decision does “Go/No-Go Criteria for Early Drug Discovery” help make?: It supports a bounded decision about whether a target or program should enter the next discovery stage. The framework keeps alternatives, evidence, counterevidence, uncertainty, and the fastest falsification test visible.
What is the fastest useful test?: require the top load-bearing claim to reproduce under an orthogonal method
Can computation validate the final scientific claim?: No. Passing an early gate does not predict eventual approval or efficacy. Computation can prioritize and eliminate directions; final validation remains with the appropriate domain methods and accountable specialists.
When should the project stop or reframe?: Stop or reframe when the target-disease link is not robust, the mechanism lacks a discriminating prediction, tractability depends on unsupported assumptions, public data contradict the thesis, or the next credible validation belongs in a qualified experimental program.