arXiv Machine Learning
Aug 12
Testing when adaptive data acquisition can replace fixed measurement plans
arXiv:2607. 27651v2 Announce Type: replace Abstract: Learned rules select samples for follow-up measurements in high-throughput experiments.
Certifying when decision-time information justifies adaptive experimentation
arXiv:2607. 27651v1 Announce Type: new Abstract: Adaptive laboratories choose measurements during experiments, yet most methods begin after adaptation is permitted.
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arXiv Machine Learning
Sep 3
Held-out evidence resolves follow-up measurement decisions in biological screens
The paper introduces OPAL, a held‑out decision test that evaluates follow‑up measurement rules in biological screens by freezing a rule and assessing unnecessary measurement, coverage, and value after cost against pre‑defined archive‑specific criteria. Using a six‑rule Cell Painting battery, the authors show that a high‑value rule would re‑image 96.01% of the library with a 97.14% false‑activation upper bound, illustrating that predicted value alone cannot justify replacing a fixed plan. In development, a sparse Cell Painting rule reduced added‑well burden 18.2‑fold but had a false‑discovery bound above 35%, leading the fixed plan to remain; similar analyses for LINCS–LJP and CTRP highlighted the need for fallback strategies and the importance of separating optimization from evidence. "whyItMatters":"OPAL provides a systematic way to determine whether a new measurement strategy truly improves experimental efficiency without compromising data quality, as demonstrated across multiple biological screening datasets."
arXiv Machine Learning
Aug 28
Predicting Quantifiability from Primary Screens to Prioritize Dose-Response Profiling
The paper introduces a framework to predict whether a compound’s potency can be quantified in dose‑response profiling, treating quantifiability as a separate triage goal from biological activity. It shows that features from low‑cost primary screens, rather than molecular structure, strongly predict quantifiability, and that this prediction holds across new chemical scaffolds and assay families. The authors argue that incorporating quantifiability predictions can better allocate expensive dose‑response resources.
arXiv Machine Learning
5d ago
Interpretable-by-Design Descriptor Portfolios Match a 2048-Dimensional Foundation Embedding on Low-Data Molecular Assays
The study evaluates whether a portfolio of compact, semantically named descriptor blocks can match the performance of a 2048‑dimensional CheMeleon embedding in low‑data molecular assays. Using a fixed 11‑dimensional physicochemical base and greedily adding provenance‑screened blocks, the portfolio achieves a mean test AUC of 0.762 across nine ADME/Tox assays, comparable to CheMeleon’s 0.764 and better than Mordred’s 0.756. The results meet a predeclared pooled parity threshold but not all per‑assay thresholds, and further analysis confirms the competitiveness of the auditable representation while highlighting unresolved assay‑level differences.
arXiv Machine Learning
Aug 31
Locked Evaluation Surfaces: Transfer Failure and Sampling-Depth Entanglement in CRISPRi Perturbation-Effect Prediction
The study evaluates a frozen Geneformer representation for predicting CRISPRi perturbation effects under a tightly controlled, pre‑registered protocol. While the representation shows significant predictive power within the Virtual Cell Challenge dataset, it fails to transfer to external screens, with negative zero‑shot Spearman correlations. The analysis also reveals that the VCC endpoint is heavily influenced by sampling depth, as cell count alone explains most of the variance, indicating a sampling‑depth entanglement that could mask transfer failures in less controlled settings.
arXiv Machine Learning
Jun 3
Fairness Definitions and Metrics in Deep Reinforcement Learning for Drug Discovery in Healthcare: A Rapid Evidence Review
arXiv:2606. 02902v1 Announce Type: cross Abstract: Deep reinforcement learning (DRL) is increasingly applied to de novo molecular design, but choices in data, rewards, and evaluation can yield uneven performance across disease areas and chemotypes.
arXiv AI
Jul 8
Evaluating calibrated refusal and safe usefulness in dual-use biology settings
arXiv:2607. 05462v1 Announce Type: cross Abstract: As AI agents are incorporated into life science workflows, the capabilities that speed discovery might also enable misuse.
arXiv Computation and Language
Sep 21
TrialAtlas: Multi-Agent Research Organization for Clinical Trial Design and Optimization
arXiv:2609.21859v1 Announce Type: new Abstract: Nearly 90% of drugs entering clinical development ultimately fail, despite billions of dollars in investment. Pharmaceutical companies therefore rely o...
arXiv Computation and Language
Sep 11
Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens
The paper introduces AssayBench-Loop, a large benchmark of 1,389 CRISPR screens across five phenotype categories, and builds on it to develop AssayLoop, a sequential experimental design framework that combines a transformer-based acquisition policy (AssayFormer) trained on historical data with LLM-derived biological priors. AssayLoop achieves a 5.67‑fold enrichment over random selection, recovering 27.7% of hits after testing only about 5% of the library, and outperforms existing adaptive-design methods and standalone LLMs. The authors also present AssayLLM, extending the approach directly to an LLM via task‑specific post‑training, and show that performance improves with more historical training data and transfers to unseen phenotype categories.
arXiv AI
Aug 11
From Trajectories to Evidence: Auditable Experimental Records for Industrial Research Agents
arXiv:2608. 05235v1 Announce Type: cross Abstract: Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajectories to guide later decisions.
arXiv AI
Jun 18
TxBench-PP: Analyzing AI Agent Performance on Small-Molecule Preclinical Pharmacology
arXiv:2606. 19245v1 Announce Type: new Abstract: Artificial intelligence (AI) agents promise to accelerate drug discovery by compressing interpretation and decision-making loops, but practical deployment requires trusted evaluation on realistic program decisions.
arXiv Machine Learning
Jul 10
Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables
arXiv:2604. 11305v3 Announce Type: replace Abstract: Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality requirement, while controlling the false discovery rate (FDR).