Scientists employ an arsenal of powerful tools during early drug discovery, generating molecular, phenotypic and computational evidence. Yet producing more data does not guarantee confident decisions about which target, compound or experiment to pursue. The constraint often lies in the gaps between tools: observations arrive in different formats, lose experimental context at handoffs and become difficult to compare or reproduce. Integration, in this sense, is a scientific problem as well as a technical one.

This article proposes a practical test for discovery workflows: can a scientist trace a consequential decision from the evidence used, through its methods and limitations, to the action taken? If that trail is weak, adding another platform or model is unlikely to repair the decision. The answer is to connect information, people and review points around a defined scientific question.

Follow the decision

Consider a team assessing whether to progress a target after a promising screening result. The initial signal might come from a biochemical assay. Subsequent evidence could include orthogonal assays, cell-based results, genetic perturbation, structural information, toxicology signals and literature. Each may sit in a separate system, owned by a different group. A decision meeting then relies on slide summaries assembled by hand.

The problem is not merely that a file was hard to find. Scientists need to know which sample and assay conditions produced a result, whether a follow-up experiment confirmed it and why conflicting evidence was discounted. Without this context, a number can appear more robust than it is. Manual reconciliation also takes time from interpretation and increases the chance of comparing unlike experiments.

A useful workflow map begins with the decision to be made rather than a list of applications. It identifies the evidence required, the source of each observation, who verifies it and the point at which a discrepancy must be resolved. This prevents integration projects from becoming mere exercises in moving data without improving a specific decision.

A useful workflow map begins with the decision to be made rather than a list of applications.

Preserve experimental context

Scientific data rarely speak for themselves. An assay result needs its protocol, controls, units, sample identity, batch, analysis version and quality flags. A structure prediction has different evidential status from an experimentally determined structure. A machine-learning score may be useful for prioritisation, but its interpretation depends on training data, applicability domain and validation.

Data should therefore travel with provenance: a record of where they came from, how they were transformed and who approved a change. Stable identifiers help connect a compound or target across teams. Agreed units, vocabularies and metadata fields make a comparison meaningful. The FAIR principles – findable, accessible, interoperable and reusable – offer a foundation for stewardship, but cannot guarantee that an underlying experiment was sound.1

The amount of metadata required should fit the decision. For a screening campaign, a minimum record might include assay version, concentration, controls, replicate count and exclusion criteria. For a medicinal chemistry handoff, compound identity, synthesis batch, purity and the exact biological readout become essential. Trying to capture every imaginable field can burden researchers; yet collecting too little creates costly ambiguity later. Teams should agree on the minimum information needed to challenge and reproduce a result.

Trying to capture every imaginable field can burden researchers; yet collecting too little creates costly ambiguity later.

Make handoffs visible

Discovery work crosses organisational boundaries. Chemistry, biology, computational science, translational research and external partners may each describe the same asset differently. Although a shared identifier is useful, it is not enough. The receiving team must understand whether the evidence is preliminary, what is still uncertain and what action is expected.

A handoff should state the scientific question, link to the underlying record, identify limitations and name the person accountable for resolving open issues. That information can be embedded in existing review routines rather than added as a separate reporting exercise. A decision log can capture the rationale for progressing, repeating or stopping work and distinguish a new result from a reinterpretation of old data.

External collaborations render this discipline especially important. Agreements should specify formats, metadata, transfer frequency, quality checks and rights to retain the scientific record. When these details are only addressed following project commencement, integration becomes a series of bespoke repairs. The objective is not to force every partner onto one platform; it is to ensure that the evidence can be understood and assessed when it crosses the boundary.

Put AI in a governed workflow

Artificial intelligence (AI) can identify patterns and help rank hypotheses, but it amplifies both the strengths and weaknesses of the information supplied to it. A model trained on inconsistent assays or incomplete metadata may produce a persuasive output that cannot be interpreted in the intended experimental setting. The advance in protein structure prediction illustrates the scientific value of a powerful computational method, while highlighting why a prediction must be understood in relation to its confidence and the biological question at hand.2

Teams must define the model’s role precisely. Is it proposing compounds for testing, flagging an anomalous result, or estimating a property? The answer determines what validation is needed and who makes the final decision. A prospective test against new experiments is more informative than a demonstration on familiar historical data. Scientists must also see when a recommendation falls outside the data on which the model has been evaluated.

An AI output should become part of a traceable decision, with its model version, input data and subsequent experimental result linked together. This helps ascertain whether the tool improved the prioritisation process or merely generated more candidates. Human challenge is not a ceremonial final approval; it is how domain knowledge, conflicting evidence and uncertainty enter the decision.

An AI output should become part of a traceable decision, with its model version, input data and subsequent experimental result linked together.

Measure decision quality

Integration projects are often judged by the number of connected systems, records moved or active users. Those measures may indicate progress, but they do not establish a scientific benefit. A more useful evaluation starts with a few decisions: target nomination, hit confirmation, lead selection or a decision to halt a programme.

Possible measures include the time needed to assemble a complete evidence package, the proportion of results with required context, the number of unresolved discrepancies at review, repeat experiments caused by missing information and the time from new evidence to a documented decision. For some programmes, later outcomes such as the reproducibility of nominated findings can be assessed. These indicators should be interpreted together. Faster decisions are not better if they are less reliable.

Faster decisions are not better if they are less reliable.

Teams can compare a baseline with a pilot in one workflow, reviewing both numbers and specific cases. A review of why scientists overrode an algorithmic ranking may reveal missing biology; a review of repeated experiments may reveal poor sample traceability. Such findings point to changes in standards, interfaces or governance. The measures should reward better evidence and clearer choices, not pressure scientists to progress weak candidates more quickly.

Reproducibility deserves particular attention. The ability to repeat an analysis depends on access to inputs, code or method versions and documented exclusions. The ability to reproduce an experimental finding depends on protocol detail, material identity and appropriate controls. These are related but distinct tests. A connected system can make both easier, but only if the records reflect real practice.

Build from one question

An organisation need not replace every discovery system to improve a decision. Instead, it can choose one consequential workflow, identify where context is lost, agree on minimum metadata and identifiers, and make the relevant evidence visible at review. Scientists and data specialists should test the change on real cases, including contradictory or incomplete results.

The pilot should also identify a clear owner for standards and exceptions. Without ownership, fields drift, local meanings diverge and an initially useful connection becomes unreliable. Governance can remain light, but someone must decide how definitions change and how corrections propagate. Feedback from users should shape the workflow after launch, because the first design will rarely capture every scientific exception.

The next gains in discovery productivity may come from making existing evidence easier to interrogate across the R&D workflow. Better integration does not eliminate uncertainty or guarantee a successful drug. It helps teams see the uncertainty, challenge an attractive signal and make the next experiment or investment with a clearer account of what is known.

References

1. Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data. 2016;3:160018. doi:10.1038/sdata.2016.18. https://www.nature.com/articles/sdata201618