Does Quantum Computing Increase Success Rates in Drug Discovery? — A Mathematical Audit of the "Score Improvement = Success" Fallacy in AI-Driven R&D

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Meta Description: Improved binding energy through quantum computing does not necessarily equate to higher success rates in drug discovery. This article mathematically demonstrates the fallacy of "score improvement = success" in AI drug discovery and presents objective audit conditions (PASS/FAIL) for claiming true progress.

A localized improvement in numerical metrics, such as "optimized binding energy," is insufficient to yield a viable drug. To accurately interpret the latest breakthroughs in drug discovery, a rigorous mathematical perspective is essential.

【Literacy Guide】 Three Perspectives for Evaluating "Quantum Drug Discovery" News

When encountering announcements regarding breakthroughs in drug discovery using quantum computers or AI, these three checkpoints help distinguish genuine progress from inflated expectations (hype).

Perspective 1: Is the metric structurally coupled to final "success" years down the line? (A: Identification) Improvements in immediate computational values do not logically necessitate the success of future clinical trials.
Perspective 2: Does the optimization of the metric account for potential increases in "unmeasured risks"? (B: Robustness) One must consider whether other vital survival conditions are compromised in the process of optimizing a specific value.
Perspective 3: Is the technology addressing the "primary bottleneck" of the process rather than just increasing calculation speed? (C: Bottleneck) Enhancing computational speed and improving the overall probability of success are mathematically distinct issues that must be decoupled.

👉 Only projects that provide clear evidence across all three perspectives are logically qualified to claim a true "improvement in success rates."

Will Quantum Drug Discovery Truly Raise Success Rates? A Mathematical Deconstruction of the Gap Between Metric Improvement and Success

In recent years, claims such as "Success rates will rise because quantum computing improves the accuracy of binding energy calculations" or "The project is advancing as AI has optimized candidate compound scores" have become prevalent.

However, when approaching such news with intellectual curiosity, we must remain cautious. These claims are difficult to derive mathematically unless specific conditions are explicitly satisfied.

This is not a denial of the potential within quantum technology or AI. Rather, it is a neutral attempt to organize the underlying logic and avoid the "structural accidents" (logical fallacies) that occur when "score improvement" is prematurely equated with "success." In this article, the GhostDrift Research Institute presents the perspectives (audit conditions) necessary to objectively evaluate progress in this field.

1. Why Quantum Drug Discovery Effects Do Not Directly Link to "Success Rates"

From a mathematical standpoint, even significant improvements in initial-stage metrics do not guarantee overall success due to three fundamental Mathematical Challenges (Logical Consequences).

Mathematical Challenge A: The Decoupling of Early and Late Stages (Non-identifiability)

The "Non-identifiability Theorem" suggests that "even if the distribution of initial data or metrics is favorable, structures always exist that allow the final success rate to be lowered arbitrarily and independently of those metrics."

Intuitive Analogy: No matter how high a student's mock exam score (initial metric) is, they will fail the actual exam (final success) if they fall ill on the test day. It is impossible to declare that "passing is guaranteed" based solely on mock exam scores. Improvement in the first half of the process does not constrain the failure risks in the second half.

Mathematical Challenge B: Optimization Bias and Declining Success (Goodhart’s Law)

Goodhart's Law—"When a measure becomes a target, it ceases to be a good measure"—can be rigorously proven mathematically. Over-optimizing a specific score (e.g., binding affinity) often leads to "Selection Bias," where candidates with risks that were never part of the metric (e.g., toxicity, poor metabolism) are inadvertently favored.

The Reality: While the average value of the metric rises, the true success rate of the "elite candidates" chosen based on that metric actually declines. This paradoxical situation is a frequent occurrence in mathematical modeling.

Mathematical Challenge C: The Inefficacy of Speed Against Late-Stage Constraints (Bottleneck Lemma)

The overall success rate of drug discovery is dictated not by the "speed" of calculation, but by the "most difficult stage to pass (rate-limiting step)" within the process.

Numerical Example: If the pass rate of a later clinical trial stage is fixed at "a few percent," that stage becomes the process bottleneck. Whether you increase computational speed by one million times using quantum technology or generate one billion candidate substances, the overall success probability is "capped (upper bounded)" by that single-digit pass rate. While faster computation contributes to engineering efficiency, it does not increase the probability of producing a drug unless it specifically improves the pass rate of that "few percent wall."

2. Why AI Drug Discovery Score Improvements Lead to "Logical Challenges"

These structural challenges are not unique to quantum technology; they are inherent in current AI drug discovery. Mistaking score improvement for "certainty of success" carries the risk of introducing fatal selection bias into a project.

If the "score" being optimized is merely a limited fragment (Drift) of the multi-dimensional and complex event of "creating a viable drug," we must remain literate enough to understand that chasing that number can actually lead us further away from the true substance of success (the Ghost).

3. Conditions for Claiming "Increased Success Rates": The Audit Protocol

For the introduction of a new technology to claim a contribution to "increased success rates"—rather than just efficiency—it must clear extremely high logical hurdles. To make these "conditional affirmations" accessible to non-experts, we have visualized them as three "Giant Walls."

🚧 The Three Walls Blocking the Claim of Success

When a news story uses the phrase "success rate is rising," it must effectively clear the following formidable challenges:

Wall 1 (A: Identification): The Valley of Time (Multi-year Gap) Initial metrics are measurable today, but success is only determined years later. This long gap is filled with countless probabilistic events—toxicity, metabolism, formulation, clinical design, and market conditions—that are often absent from the computational model.
👉 "To guarantee a pass rate for an exam five years away based on today's mock exam scores, one must perfectly incorporate every possible unforeseen event occurring over those five years into the model." This is an extremely difficult requirement. (Difficulty: ★★★★★)
Wall 2 (B: Robustness): Optimization-Induced Side Effects Optimizing a specific score tends to manifest unmeasured risks. To prevent this, one needs powerful constraints that logically guarantee side effects will not increase in exchange for a higher score.
👉 This is similar to the challenge: "There are forms of education where chasing only test scores destroys a student's character. You must design a 'meta-rule' that maintains character while raising scores." (Difficulty: ★★★★☆)
Wall 3 (C: Upper Bound): Proof of Breaking the "Few Percent" Wall No matter how much you increase computational resources in the early stages, the success rate will not rise if the later bottleneck remains unchanged. The focus should not be on "computational speed," but on the actual measurement of "how many percentage points the success rate (ε) of the bottleneck stage was improved."
👉 This is the fundamental question: "Gathering ten times more people at the entrance does not increase survivors if only 1% can pass through the exit. Show us the specific plan to expand that 1% exit to 2%." (Difficulty: ★★★★★)

Based on these walls, the GhostDrift Research Institute defines the following "Audit Protocol" as the standard for evaluating true success.

✅ A. Identification Audit

"Is the metric structurally coupled to the final success?" There must be an explicit link between the initial calculation and success years later, established through a structural mathematical model rather than mere correlation.

✅ B. Goodhart Robustness Audit

"Have you excluded 'inversion regions' where optimizing the score causes other risks to explode?" This perspective verifies whether "distortions" that compromise the true goal (success) are occurring during the optimization process.

✅ C. Bottleneck Audit

"How much did the technology specifically improve the success rate of the process bottleneck?" This requires measurements of moving the "probability wall" itself, rather than the "engineering metric" of shortened calculation time.

4. Conclusion: Quantum Drug Discovery Can Only Discuss Success Rates "If Conditions are Met"

The conclusion of this article is quite simple:

PASS (Success rate improvement can be claimed): If all conditions A, B, and C are met, the technology is mathematically supported as "contributing to the success rate of drug discovery."
FAIL (Success rate improvement cannot be scientifically mentioned): If even one is missing, the "logical necessity of success rate improvement" cannot be scientifically claimed.

No matter how sophisticated the quantum algorithms used, equating local metric improvement with "drug discovery success" risks fueling excessive expectations. Any claim that does not meet these rigorous "conditions" should, at this stage, be considered mathematically unjustifiable.

FAQ: Common Misunderstandings

Q: Does this mean quantum drug discovery is meaningless?
- A: Not at all. Faster calculations and higher precision have significant engineering value. However, claiming that these directly lead to "improved overall success rates" requires an extremely careful and rigorous logical explanation.
Q: Does AI drug discovery face the same challenges?
- A: Yes. In fact, large-scale optimization by AI is more prone to the "score improvement = success" fallacy because it can happen without human realization, necessitating even more vigilant oversight.
Q: Where should the true value of quantum computing be sought?
- A: Quantum technology will become a powerful weapon that changes the history of drug discovery when it can be demonstrated how reduced estimation errors and higher predictive precision specifically act upon the "process bottleneck."

"Conditions" Before Stories

When we encounter grand visions of what quantum technology might make possible, we must calmly examine the "mathematical conditions necessary to claim success." A story of success without clear conditions is nothing more than a Drift without substance. To avoid losing sight of the true entity (the Ghost) behind the numbers, we intend to maintain a perspective of rigorous yet helpful audit.