Reliability & Governance Standard for CLIP-Based Virtual Drug Screening

Edge of Knowledge · Methods & Standards · Public Draft v1.0

Purpose

This document defines a set of non-expansive, implementation-agnostic operational standards intended to improve the reliability, reproducibility, interpretability, and auditability of CLIP-based virtual drug screening pipelines. It addresses recurring failure modes observed in large-scale computational screening without altering underlying scientific claims or substituting for experimental validation.

Scope

These standards apply to virtual screening systems that employ joint protein–ligand embedding or similarity-based retrieval architectures, including CLIP-inspired models. The controls described herein operate at the workflow and system-behavior level and are designed to coexist with diverse model architectures, datasets, and institutional environments.

Enumerated Controls

1. Retrieval Calibration Layer

Post-retrieval similarity scores SHOULD be calibrated (e.g., via isotonic or temperature scaling) prior to final candidate ranking. Calibration mitigates overconfident embedding tails and stabilizes ranking behavior across targets and repeated runs.

2. Negative Sampling Discipline

Training pipelines SHOULD incorporate disciplined hard-negative sampling using structurally plausible non-binders. This control improves discrimination beyond naive similarity and reduces false-positive enrichment.

3. Pocket–Ligand Interaction Decomposition

Candidate scores SHOULD be decomposable into interpretable components, such as pocket geometry alignment, chemical feature compatibility, and learned interaction residuals. This enables expert review and mechanism-based triage.

4. Failure-Mode Logging

Systems SHOULD log exclusion and rejection events with explicit reasons, including confidence thresholds, uncertainty measures, and score variance. Failure-mode logging supports iterative improvement and transparent error analysis.

5. Determinism Envelope

A determinism mode SHOULD be available, enforcing fixed random seeds, frozen model weights, and pinned dependencies. This enables exact reproducibility for demonstrations, cross-institutional comparison, and regulatory review.

6. Golden-Set Regression Tests

Screening pipelines SHOULD maintain non-negotiable regression tests against benchmark protein–ligand pairs. Golden sets enforce non-regression guarantees and defend against silent performance drift during system evolution.

Explicit Boundaries

• These standards do not introduce autonomous decision-making or validation.
• Computational outputs are not treated as biological or clinical conclusions.
• Wet-lab validation and expert review remain mandatory downstream steps.
• No performance, speed, scale, or discovery claims are asserted.

Status & Versioning

This document is released as a public draft (v1.0). It is intended to be versioned, auditable, and incrementally refined in response to community, institutional, and regulatory needs while preserving its non-expansive scope.

Citation

When referencing this standard, cite as:
“Reliability & Governance Standard for CLIP-Based Virtual Drug Screening, Edge of Knowledge, Moral Clarity AI, v1.0.”