The Tool Desk
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Where AI fits in the drug-discovery process
Drug discovery is a sequence of connected decisions, not one computational task. Researchers first investigate disease biology and potential targets, then identify or design candidate molecules, test them in experiments, and advance selected candidates through preclinical studies and clinical trials. AI can assist at several points, but the evidence needed changes at each step.
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- Understand biology: analyze literature, experimental data, images or predicted protein structures to propose targets and mechanisms.
- Find candidate molecules: generate structures, search compound collections or estimate which candidates merit laboratory testing.
- Test and refine: synthesize or obtain candidates, run assays and compare observed results with computational predictions.
- Develop and evaluate: assess safety and effectiveness through preclinical work and clinical trials; computational models may support parts of this process.
A model output is useful when it helps decide what to test next. It does not replace the experimental or clinical evidence needed to establish whether a candidate is a medicine.
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Ten representative tools and approaches
The list brings together products and tool categories described in a 2026 survey. It is an editorial selection, not an official top-ten list or a claim that all tools have the same level of evidence. Their outputs also differ: a structure prediction, a target hypothesis and a trial model cannot be judged by the same success measure.
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| Workflow area | Tool or approach | What it contributes | What must still be established |
|---|---|---|---|
| Protein structures | AlphaFold | Predicted protein structures that can inform structural biology and downstream discovery. | Whether a prediction is suitable for the particular biological question and whether experiments support the resulting hypothesis. |
| Target identification | PandaOmics | Target-identification capability within Insilico Medicine’s Pharma.AI offering. | Whether the proposed target is relevant to disease and can be acted on safely and effectively. |
| Molecule generation | Chemistry42 | Molecule-generation capability within Pharma.AI. | Whether generated candidates can be made, show the desired activity and meet later safety and development requirements. |
| Virtual screening | Atomwise’s AtomNet | Computationally ranks candidate compounds against targets for follow-up. | Whether ranked compounds confirm activity in assays and can progress through development. |
| Phenomics | Recursion | Automated experiments and image-based analysis to identify biological patterns. | Whether an observed pattern has a causal, reproducible and therapeutically useful explanation. |
| Knowledge integration | BenevolentAI | Combines biomedical relationships to support target or drug-repurposing hypotheses. | Whether the relationships and proposed mechanism withstand expert review and experimental testing. |
| Computational chemistry | Schrödinger software | Molecular modeling that combines physics-based and machine-learning approaches. | Whether model assumptions and predictions fit the system and are confirmed experimentally. |
| Research informatics | Benchling AI | AI assistance for literature or experiment-data workflows. | Whether outputs are accurate and appropriate for the research task; assistance is not evidence of drug efficacy. |
| Research informatics | General biomedical language models | Can help process biomedical information and support research workflows. | Whether generated or extracted information is correct, traceable and validated by qualified users. |
| Clinical-trial modeling | Unlearn | A digital-twin approach described for supporting trial design. | Whether the model is credible for its intended use and whether any claimed trial benefit is demonstrated. |
Structures and target hypotheses
AlphaFold represents a structure-prediction approach: it estimates a protein’s structure, which can help researchers reason about biology and plan follow-up work. A 2024 review reported more than 214 million predicted structures in AlphaFold DB. That is a dated figure reported by the review, not a live count or a measure of how many predictions have led to useful medicines.
PandaOmics and BenevolentAI address different routes to a target hypothesis. The former is described as target-identification software; the latter uses biomedical relationships to help identify target or repurposing possibilities. In either case, a plausible connection assembled from data is a starting point for biological review and testing, not proof that intervening on the target will help patients.
Generating and ranking molecules
Chemistry42 is described as Pharma.AI’s molecule-generation component. A generated structure is a candidate idea, not an experimental result. Researchers still need to determine whether it can be synthesized, behaves as intended in relevant assays, and has acceptable properties for further development.
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AtomNet represents virtual screening: computation helps rank compounds for testing against a target. Ranking can help prioritize a search, but it is not the same as synthesizing or obtaining a compound, confirming activity in an assay, or showing clinical benefit.
Learning from experimental patterns and molecular models
Recursion’s phenomics approach starts from automated experiments and image-based analysis. Instead of beginning only with a named target and asking which molecule may affect it, this kind of workflow looks for patterns in observed cellular phenotypes that may point to biological relationships. A pattern still needs follow-up to establish what caused it and whether it matters for a disease.
Schrödinger illustrates computational chemistry that combines physical modeling and machine-learning methods. Compared with data-driven molecule generation, physics-informed approaches make explicit use of molecular and physical modeling assumptions. Neither approach removes the need to test predictions against experiments; their relative usefulness depends on the question, available data and validation pathway.
Rank #3
Supporting research and clinical workflows
Benchling AI and general biomedical language models can assist with literature or experiment-data tasks. They are enabling tools: a summary, extraction or generated answer should be checked against reliable source material and the underlying experimental record. Such assistance does not establish that a compound works.
Unlearn is described as using digital twins in trial design. The reviewed material does not establish that the approach is FDA-approved or that it has been proven to reduce trial sample sizes. Its output should be evaluated for the particular proposed use rather than treated as a general guarantee of faster or smaller trials.
What the Rentosertib example does—and does not—show
A 2026 survey reports that Insilico Medicine’s Rentosertib program, also known as ISM001-055, took 18 months from target discovery to Phase I. This is a reported, case-specific timeline. It illustrates a program in which AI was part of the process; it does not establish that AI generally takes drug discovery from target to human testing in 18 months, nor does the timeline by itself show clinical effectiveness or comparative superiority.
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How to judge evidence and regulatory relevance
Different outputs require different checks. A predicted structure may be assessed against structural or experimental evidence; a target hypothesis needs biological validation; a ranked or generated molecule needs synthesis or access followed by relevant assays; and a model used in clinical development needs evidence appropriate to its role. A software demonstration or vendor-reported result is not interchangeable with independently published findings or clinical evidence.
The FDA’s January 2025 draft guidance, Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products, is explicitly nonbinding and marked “Not for implementation.” It proposes a risk-based credibility assessment tied to the model’s specific context of use when AI outputs support regulatory decisions about safety, effectiveness or quality. It is proposed guidance, not a final rule or an endorsement of a named platform.
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How to choose a tool for a research program
Start with a specific bottleneck rather than a broad promise to “use AI.” The right fit depends on what decision the tool is meant to improve and what evidence can be gathered to check it.
- Define the context of use. Specify the biological or development question, who will use the output and what decision it is expected to inform.
- Check the inputs. Ask what data the method needs, how representative and reliable those data are, and whether the team can access them.
- Identify the output and validation path. Decide what observation would confirm or refute the prediction, such as a relevant assay, replicated phenotype or other suitable evidence.
- Inspect evidence quality. Distinguish vendor descriptions from published studies and clinical evidence; look for evidence that matches the intended use rather than an adjacent task.
- Assess interpretability and credibility. Understand the assumptions, uncertainty and failure modes that matter for the decision. For regulatory-facing use, align credibility assessment with the specific context of use.
- Plan integration. Account for how results will fit existing data systems, laboratory work, expert review and documentation—not just whether a model can produce an output.
No single “best” tool follows from the examples here. A structure predictor, phenomics platform, virtual screen and clinical-trial model solve different problems; compare tools within the workflow stage and evidence standard that matter to the program.
Quick Recap
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