REVIEW 8 cited by
DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Recent progress in Large Language Models (LLMs) has drawn attention to their potential for accelerating drug discovery. However, a central problem remains: translating theoretical ideas into robust implementations in the highly specialized context of pharmaceutical research. This limitation prevents practitioners from making full use of the latest AI developments in drug discovery. To address this challenge, we introduce DrugAgent, a multi-agent framework that automates machine learning (ML) programming for drug discovery tasks. DrugAgent employs an LLM Planner that formulates high-level ideas and an LLM Instructor that identifies and integrates domain knowledge when implementing those ideas. We present case studies on three representative drug discovery tasks. Our results show that DrugAgent consistently outperforms leading baselines, including a relative improvement of 4.92% in ROC-AUC compared to ReAct for drug-target interaction (DTI). DrugAgent is publicly available at https://anonymous.4open.science/r/drugagent-5C42/.
Forward citations
Cited by 8 Pith papers
-
Inside the Unfair Judge: A Mechanistic Interpretability Account of LLM-as-Judge Bias
LLM-as-judge scoring biases concentrate in low-dimensional, type-specific activation subspaces that support bidirectional causal steering and cross-domain failure prediction.
-
Studying quantization trade-offs for efficient inference deployment in machine translation
Quantized Hy-MT2 models stay accurate at long context, but quantized EuroLLM 9B/22B models collapse (up to ~60% chrF++ drop) while W4A8/W8A8 plus 200–400-token chunking improves serving throughput.
-
A Precedent-Guided Co-Scientist for Side-Effect-Aware Drug Redesign
PRECEDE redesigns parent drugs to mitigate a specified side effect by classifying liability mechanism, transferring strategies from historical precedents, and ranking candidates with in silico proxies under human checkpoints.
-
DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search
A multi-agent, retrieval-augmented search framework lets a 7B language model outperform larger baselines on drug-target interaction prediction, but its headline recall relies on knowing the ground-truth output count.
-
Evaluating Agentic Bioinformatics through Function, Evidence, and Validation
Agentic bioinformatics systems mostly demonstrate planning and tool execution but rarely prospective empirical validation, so the paper argues evaluation should center on inspectable workflow trajectories (FEV) rather...
-
Valid Property-Enhanced Contrastive Learning for Targeted Optimization & Resampling for Novel Drug Design
VECTOR+ combines contrastive learning and Gaussian mixture sampling to generate novel, synthetically plausible inhibitors from low-data datasets, with improved docking scores over known compounds.
-
Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists
MAPPS combines LLM workflow planning, code generation, and human intuition with machine-learned force fields to discover crystal structures, reporting high stability and novelty rates on MP-20 and Matbench.
-
AI Scientists Fail Without Strong Implementation Capability
AI scientist systems can propose ideas but cannot reliably implement and verify experiments, making the implementation gap, not idea generation, the current bottleneck.
Discussion (0). Continue with ORCID to comment.