Biopharma teams routinely face high-stakes prioritization decisions that can determine the success or failure of key programs in their pipeline, ranging from which drug targets are worth pursuing to how best to balance competing elements of a clinical strategy. While data availability continues to increase, many organizations lack a structured, transparent process for translating evidence into consistent and defensible decisions.
Traditional approaches such as multi-criteria decision analysis and weighted scorecards are widely used but often rely on subjective weighting, inconsistent criteria, and simplified scoring systems that may not adequately reflect uncertainty. As a result, decision processes can become difficult to defend, challenging to audit, and prone to internal debate. In modern bioinformatics, a common computational output is a target list, a simple ranking of biological entities or genes based on a signal like effect size.
However, simply pursuing the strongest biological signal may not be the best business or clinical strategy. Stakeholders must balance numerous factors that may be difficult to quantify. For instance, in choosing a drug target, considerations could include disease association, druggability, potential safety risks, competitive landscape, patentability, and the regulatory pathway. Deciding which targets, clinical research organizations, or pipeline programs to fund aggressively often forces organizations into a corner, relying on flawed evaluation methodologies.
Rather than compressing uncertainty into a single composite score, biopharma teams need to utilize tools that make underlying assumptions and evidence drivers explicit, enabling stakeholders to understand what influences a ranking and what conditions could alter an outcome. This way, insights lead to more than just probability outcomes and instead provide clear and decisive decision making that can be defended when subjected to the intense scrutiny that is expected of drug targets in a clinical paradigm. The right AI tools can facilitate this process.
The Pitfalls of Arbitrary Scoring
Historically, prioritization decisions in biopharma have been managed by committee, ad hoc judgments, or complex spreadsheets. This traditional approach typically suffers from a number of issues that lead to indefensible insights or too many targets for a biotech company to realistically approach.
One issue arises when evaluators in these situations, especially with black box AI solutions that are trying to white label solutions across industries, assign arbitrary weights to different criteria. These numbers seldom represent real scientific properties and act as poor proxies for expressing difficult qualitative concepts. Organizations often use a 1-to-5 scoring system for each target across various priorities, calculating a weighted average to dictate a final choice. This kind of subjective scaling can have vastly different results depending on the scale used and can be difficult to defend.
This is complicated by the fact that the poor auditability of the decisions makes the heuristic scoring model less effective. Deciphering exactly why a particular decision was made or what evidence backed a specific 1-to-5 score is notoriously difficult.
When the choices made as a result of these scoring insights can cost a clinical program millions of dollars and years of effort potentially chasing a flawed target, clear and defensible choices are essential.
The Paradigm Shift: Defensible Decision-Making
Defensible decision-making reimagines this process by asking qualitative questions that result in qualitative answers, which are then processed into robust, defensible recommendations that can be examined and scrutinized at every level. In direct contrast to simple scoring system or weighted average, a defensible decision-making AI report will drill down into every decision made amongst a series of targets and the reasons they were chosen.
This approach begins by defining a clear decision context and establishing distinct criteria (‘arenas’), such as translational feasibility, safety and selectivity, or commercial opportunity. These arenas and their definitions are what allows clinicians to remain in control of the process. Instead of relying on a numerical scale, clinical teams can define decision context for the platform so it can get to the heart of what your team is trying to determine and show it’s work on how it got there.
For an example, if a clinical team is choosing drug targets, they might want to give preference to safety over drug ability or the amount of known evidence over therapeutic differentiation for patients. Once you have defined this important context, the AI system can conduct scalable pairwise competitions such as asking, “which target has a safer profile for chronic use, A or B?” in the context that the clinical team has clearly defined. These scalable pairwise competitions, and the outcomes determined from them, are all detailed in the AI system, providing important reasoning for why some outputs are ‘winning’ and others are ‘losing.’
Every comparison and its underlying rationale can be examined with this kind of tool. Users can drill down into reasoning dossiers to understand exactly why one target won against another. Defensible systems track statistical noise to detect faulty logic, flagging areas where the AI judge lacks enough data or reasoning capacity and allowing the team to debug the workflow and provide the necessary information to avoid a shot in the dark.
The AI in this scenario conducts comprehensive research for each item within a specific arena, in as little as five minutes, digesting user-provided data, databases, and literature to build robust evidentiary profiles. It also intentionally builds a case against an item to highlight risks and holes in the literature. Statistical models then aggregate these qualitative matchups into a quantified ranking that can be uncovered to provide insights into why the system came to that conclusion.
Lastly, in order to more properly fit into the existing biopharma or biotech workflow, the AI system should act as a complement, not a replacement. Human experts can retain control by defining the decision context, setting operational priorities, supplementing research gaps, and editing the criteria, as needed. The system clearly visualizes how shifting human priorities like valuing mechanistic evidence over commercial opportunity dynamically alters the final insights. As a result, instead of arbitrary outputs, users can determine why these findings were chosen and use this information to justify their choices with much more complete information.
What Lies Ahead
When the stakes are high, such as allocating millions of dollars to a clinical program or selecting the right drug target, relying on the arbitrary scoring of a spreadsheet introduces unmanageable risk. Defensible decision-making through understandable AI and an insightful platform that can handle the immense scale of qualitative research and pairwise comparisons is ideal for highlighting key insights while keeping human expertise at the helm. By replacing subjective numbering with transparent, auditable, and stress-tested debate, biopharma organizations can ensure their critical decisions are not just calculated but truly justified.
As we move into the future, education around AI tools must continue. Stakeholders must be given the tools they need to make smart decisions when the future of their programs is in the balance. To date, our industry has failed far too often, leading to commitment of resources to programs that ultimately fail in the middle of the drug development lifecycle.
About Vin Singh
Vin Singh, Chairman and CEO of BullFrog AI, is a serial entrepreneur with extensive experience in the healthcare industry. Previously, he was the Founder & former CEO at Next Healthcare Inc., Co-founder of MaxCyte Inc. (Nasdaq: MXCT), and Global director of cell therapy at ThermoFisher Scientific, among others. Vin earned his B.S. in electrical engineering at Rutgers University, an M.S. in biomedical engineering at Rensselaer Polytechnic Institute, and an MBA at Johns Hopkins University.
