Yousefabadi, H. | Search bioRxiv

bioRxiv2026

BioTrouble: A Multi-Agent Workflow for Troubleshooting Molecular Biology Techniques

Troubleshooting is a critical yet often underdocumented aspect of molecular biology experiments across laboratories. Failures in core techniques such as PCR, qPCR, molecular cloning, and related assays can lead to experimental failure, wasted resources, and delays in research progress. Here, we present BioTrouble, a multi-agent AI workflow designed to assist researchers in troubleshooting a wide range of molecular biology experiments. It leverages a custom-designed troubleshooting knowledge base through a retrieval-augmented generation (RAG) framework. BioTrouble employs small language models to generate the troubleshooting plan and utilizes a smart model routing system to manage cost per request. User interactions and feedback are stored as structured cases, enabling BioTrouble to expand its troubleshooting knowledge base and improve response generation over time. Compared with single-model SOTA LLM, BioTrouble generated comparable troubleshooting recommendations using small language models.

bioinformatics↗

bioRxiv2025

Bioactivity-Driven Prediction of Antibacterial Synergy Using Machine Learning Models

MotivationPredicting antibacterial drug synergy remains difficult due to strain variability and the limited scale of experimentally tested combinations. Existing machine-learning approaches often rely on permissive cross-validation schemes that allow drug pairs to appear across folds, inflating performance. A rigorous evaluation framework and scalable feature representation are needed for robust generalization. ResultsWe assembled a curated dataset of 3,160 drug-pair-strain interactions covering 97 compounds and 10 bacterial strains. We then developed HALO (Held-out Antibiotic interaction Learning from latent bioactivity Observations), a synergy-prediction framework in which each drug pair is encoded using multi-level Chemical Checker (CC) similarity features spanning chemical, target, network, cellular, and clinical bioactivity domains. Under strictly nested, pair-heldout cross-validation (CV1), HALO achieved stable generalization to unseen combinations (accuracy {approx} 0.75; ROC-AUC = 0.82). Performance depended strongly on evaluation stringency: models performed well under random splits but degraded when required to generalize to unseen drug pairs and strain contexts. Despite these constraints, HALO generalized to an independent set of Loewe- measurements, achieving ROC-AUC = 0.85 for distinguishing synergy from antagonism. These results demonstrate that multi-level bioactivity signatures provide a scalable, interpretable basis for predicting antibacterial synergy and reveal the performance limits of current models under rigorous evaluation. Availability and ImplementationCode, data-processing scripts, and trained models will be available at GitHub repo. Contactmehrmohamadi@ut.ac.ir Supplementary informationSupplementary figures and additional evaluation details are available online.

bioinformatics↗