How AI Drug Discovery Works: From Protein Structure Prediction to Candidate Drugs

AI drug discovery is a way to help researchers choose biological targets, predict how molecules may interact, and prioritize compounds for laboratory testing. It does not turn a protein structure prediction into a finished medicine: each computational suggestion is a hypothesis that must be tested, optimized, and evaluated for safety and effectiveness.

What AI drug discovery does—and what it cannot establish

A drug-discovery project begins with a biological question: which part of a disease process might be changed to produce a useful effect? A target is often a protein or another biological component thought to be involved in that process. Researchers need evidence that the target matters, not just a model that can describe it. AI can help analyze biological data and prioritize possible targets, but the examples discussed here chiefly show structure prediction and molecule design; they do not show that AI can automatically validate a target.

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Once researchers have a target hypothesis, computational tools can help describe its molecular context and propose or rank possible compounds. The value is practical: models can help focus experimental work on selected questions and molecules. The result, however, is not proof that a compound binds in a living system, changes the disease process, is safe, or benefits patients. Those are distinct questions requiring appropriate experiments and, ultimately, clinical evidence.

It helps to keep the stages separate. A predicted structure is a computational model. A hit is an early compound that produces a measured result in a specified assay. A development candidate is a molecule that has survived further optimization and evaluation for the intended next stage. A proven treatment requires evidence that it is safe and effective for people in its particular use. These labels are not interchangeable, and a promising result at one stage does not guarantee the next.

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How protein structure prediction helps researchers design drugs

Proteins are three-dimensional molecules, and their shapes and interactions can help researchers reason about where and how another molecule might bind. A structure can therefore inform hypotheses about a target and help guide structure-based drug design. It is a map for asking questions, not a laboratory measurement of a proposed drug’s effect.

AlphaFold 2 predicts protein structures from sequence. AlphaFold 3 expands the modeling scope: it can predict joint structures involving proteins, DNA, RNA, small molecules called ligands, and other biomolecules. Google DeepMind and Isomorphic Labs describe its method as an improved Evoformer-based system that processes inputs and uses a diffusion network to assemble predicted structures. The aim is to model how the supplied molecular components may fit together, which can help researchers form hypotheses about interactions relevant to drug design. Google DeepMind and Isomorphic Labs, May 2024

That added scope matters because drug-design questions often concern a molecule in relation to a biological target, rather than a protein in isolation. A predicted joint structure may suggest a possible binding arrangement or help a team decide which compounds to investigate. It still does not establish that the arrangement occurs under experimental conditions, that binding is strong enough to matter, or that the molecule produces a useful biological effect. The answer depends on the question being asked and on experimental validation.

The scale of the public AlphaFold resource is notable, but scale should not be mistaken for clinical evidence. Google DeepMind says its AlphaFold database contains more than 200 million predicted protein structures; the structures are research resources, not proof that a drug exists or works. The AlphaFold Server is described as offering prediction capabilities for non-commercial research, so users should not infer that this description grants commercial-use rights. Google DeepMind AlphaFold project and the AlphaFold 3 announcement

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The AI-assisted drug-discovery pipeline

1. Select and prioritize a target

Researchers first ask whether changing a particular target could plausibly affect a disease process in a useful way. Biological data and computational analysis can help compare hypotheses or identify patterns worth investigating. The target still needs scientific justification and validation; a model ranking is not, by itself, evidence that acting on the target will help patients.

2. Model the target and its molecular context

Researchers can use experimental structures, predicted structures, or other evidence to understand a target and consider possible interaction sites. A protein-only prediction and a joint prediction that includes a ligand address different modeling scopes. Neither output should be treated as an observed binding event. The team must decide whether the prediction is suitable for its particular question and then test important implications experimentally.

3. Generate or rank molecules

Drug-design workflows may use structure hypotheses alongside biological or chemical data to propose compounds or prioritize existing possibilities. Generative models can suggest molecular structures; ranking tools can help decide which suggestions to examine first. These steps narrow a search, but a proposed molecule must still be chemically feasible, synthesized, and measured. A generated structure is not automatically a usable drug candidate.

4. Make the compounds and test them

Scientists synthesize selected molecules and run assays suited to the question—for example, whether a compound binds a target under specified experimental conditions. If results are weak or absent, that information can guide a revised design. If an assay reports a hit, further work is required to determine what the measurement means and whether the compound has other properties needed for development.

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5. Optimize and evaluate before clinical testing

Researchers must investigate characteristics such as absorption, distribution, metabolism, and excretion (ADME), as well as safety, selectivity, and efficacy. Selectivity asks, in part, whether a molecule acts on its intended target rather than producing unwanted effects through other targets. The necessary evidence and decisions depend on the intended context; a favorable binding result alone does not settle them.

Only after further preclinical work can a candidate be considered for clinical testing. Studies in people are needed to assess whether the intervention is safe and effective in the relevant population and use. A computational result, a binding assay, or an early research hit cannot substitute for that evidence.

What a reported CDK20 study shows—and does not show

A 2022 preprint illustrates the transition from prediction to experiment. The researchers investigated CDK20, a target discussed in the context of hepatocellular carcinoma, using an AlphaFold-predicted structure alongside the PandaOmics biocomputational platform and the Chemistry42 generative chemistry platform. In their reported workflow, PandaOmics provided targets of interest and Chemistry42 generated molecules based on the predicted structure. This is one company’s reported approach, not a universal recipe or independent proof that any single platform discovers drugs.

In the first round, the team synthesized seven compounds and tested them in biological assays. The report describes a first hit with a measured binding affinity. The researchers then generated and synthesized six additional compounds in a second round and reported a hit with stronger binding. The paper does not make those outcomes equivalent to clinical benefit: the reported results are binding measurements in an early research workflow, not evidence that a compound treats cancer in people. InSilico Medicine researchers, 2022

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The authors said that further optimization, ADME work, and kinase selectivity evaluation were ongoing. Those outstanding steps are central to interpreting the example. The report demonstrates a prediction-to-experiment feedback loop—model, make, measure, and use results to inform another design round—but it does not establish a finished development candidate, an approved medicine, or clinical efficacy. It is a 2022 preprint and should be read as an early-stage research report.

How to read the evidence at each stage

When reading an AI drug-discovery claim, check what was actually measured. Was the output a predicted structure, a proposed molecule, a binding assay result, or evidence from a later stage? Which target and experimental context were involved? Was the work reported in a preprint, and what follow-up remained? Keeping those distinctions visible prevents a model’s output from being described as a drug discovery success before the evidence warrants it.

What FDA’s draft guidance says about AI evidence

AI-generated information used to support decisions about drug safety, effectiveness, or quality raises a context-specific question: how credible is the model for the particular use being made of it? In January 2025, the FDA issued draft guidance proposing a risk-based framework for assessing model credibility in a defined context of use. The document is explicitly a draft, is non-binding, and is not for implementation; it should not be described as a finalized rule. FDA draft guidance, January 2025

The context-of-use idea is important: credibility is not a blanket quality label that makes a model reliable for every task. A model used to help prioritize a research experiment makes a different claim from one used to support a regulatory decision. The FDA’s AI drug-development page reports more than 500 submissions with AI components from 2016 to 2023 and more than 800 comments on its May 2023 AI discussion paper. Those figures indicate regulatory and stakeholder engagement, not proof that AI-enabled medicines are more successful or that every submitted model is credible for every use. FDA, Artificial Intelligence in Drug Development

What AI changes in practice

AI can help researchers organize complex data, generate hypotheses, and decide which structures or compounds merit attention. In structure-informed design, its contribution may be to propose a plausible interaction or molecular design that a team can test. Experimental results then constrain the next round of reasoning: an unsuccessful molecule provides evidence about that design, while a measured hit provides a reason to investigate further.

The central practical advantage is therefore not that computation removes experiments. It is that computational models can help direct experiments and support iteration. How much they help depends on the quality and relevance of the inputs, the validity of the model for its use, the ability to synthesize and test its suggestions, and the evidence needed for the intended therapeutic claim. No comparative clinical success-rate figure is established here, so a broad claim that AI makes drug development more likely to succeed would go beyond the cited evidence.

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How to interpret a claim that AI found a drug

• Ask what was found. A predicted structure, a generated molecule, a laboratory hit, and a medicine are different outcomes.
• Look for the experiment. A model prediction is not a binding measurement; a binding result is not proof of efficacy or safety.
• Check what remains. Optimization, ADME, selectivity, safety, and efficacy may still need evaluation after an early hit.
• Read the stage and source. An early preprint should not be presented as evidence of clinical success or regulatory approval.
• Match confidence to use. The FDA’s January 2025 draft framework proposes evaluating credibility for a particular context of use, rather than assuming a model is reliable for every decision.

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