DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery

• URL: http://arxiv.org/abs/2604.02346v1
• Date: Wed, 11 Feb 2026 19:16:33 GMT
• Title: DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery
• Abstract summary: Large language models (LLMs) are in the ascendancy for research in drug discovery.<n>DrugPlayGround is a framework to evaluate and benchmark LLM performance.
• Score: 11.058244885471169
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are in the ascendancy for research in drug discovery, offering unprecedented opportunities to reshape drug research by accelerating hypothesis generation, optimizing candidate prioritization, and enabling more scalable and cost-effective drug discovery pipelines. However there is currently a lack of objective assessments of LLM performance to ascertain their advantages and limitations over traditional drug discovery platforms. To tackle this emergent problem, we have developed DrugPlayGround, a framework to evaluate and benchmark LLM performance for generating meaningful text-based descriptions of physiochemical drug characteristics, drug synergism, drug-protein interactions, and the physiological response to perturbations introduced by drug molecules. Moreover, DrugPlayGround is designed to work with domain experts to provide detailed explanations for justifying the predictions of LLMs, thereby testing LLMs for chemical and biological reasoning capabilities to push their greater use at the frontier of drug discovery at all of its stages.

Related papers

• DrugReason: Dynamic Multi-View Reasoning over Knowledge Graph and Language Evidence for Drug Repurposing [55.45112712244231]
  DrugReason is a multi-view reasoning framework that integrates grounded KG reasoning with LLM-generated mechanistic inference for drug repurposing.<n> Experiments on PharmaDB, DDInter, and DrugBank show that DrugReason improves average performance over strong single-view reasoning baselines.
  arXiv  Detail & Related papers  (2026-09-06T18:54:26Z)
• LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions [80.12078194093013]
  We present the first comprehensive survey of hallucinations in LLM-based agents.<n>We propose a new taxonomy that identifies different types of agent hallucinations occurring at different stages.<n>We conduct an in-depth examination of eighteen triggering causes underlying the emergence of agent hallucinations.
  arXiv  Detail & Related papers  (2025-09-23T13:24:48Z)
• DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search [10.123162419093973]
  DrugMCTS is a novel framework that integrates RAG, multi-agent collaboration, and Monte Carlo Tree Search for drug repositioning.<n>It employs five specialized agents tasked with retrieving and analyzing molecular and protein information, thereby enabling structured and iterative reasoning.<n>Our results highlight the importance of structured reasoning, agent-based collaboration, and feedback-driven search mechanisms.
  arXiv  Detail & Related papers  (2025-07-10T04:39:55Z)
• PharmAgents: Building a Virtual Pharma with Large Language Model Agents [19.589707628042422]
  We introduce PharmAgents, a virtual pharmaceutical ecosystem driven by multi-agent collaboration.<n>The system integrates explainable, LLM-driven agents equipped with specialized machine learning models and computational tools.<n>It identifies potential therapeutic targets, discovers promising lead compounds, enhances binding affinity and key molecular properties, and performs in silico analyses of toxicity and synthetic feasibility.
  arXiv  Detail & Related papers  (2025-03-28T06:02:53Z)
• DrugImproverGPT: A Large Language Model for Drug Optimization with Fine-Tuning via Structured Policy Optimization [53.27954325490941]
  Finetuning a Large Language Model (LLM) is crucial for generating results towards specific objectives.<n>This research introduces a novel reinforcement learning algorithm to finetune a drug optimization LLM-based generative model.
  arXiv  Detail & Related papers  (2025-02-11T04:00:21Z)
• Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials [49.19897427783105]
  The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift. We investigate how these advanced computational models can uncover target-disease linkage, interpret complex biomedical data, enhance drug molecule design, predict drug efficacy and safety profiles, and facilitate clinical trial processes.
  arXiv  Detail & Related papers  (2024-09-06T02:03:38Z)
• Large Language Model Distilling Medication Recommendation Model [58.94186280631342]
  We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs)<n>Our research aims to transform existing medication recommendation methodologies using LLMs.<n>To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model.
  arXiv  Detail & Related papers  (2024-02-05T08:25:22Z)
• KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description [11.64859287146094]
  We propose KGML-xDTD: a Knowledge Graph-based Machine Learning framework for explainably predicting Drugs Treating Diseases. We leverage knowledge-and-publication based information to extract biologically meaningful "demonstration paths" as the intermediate guidance in the Graph-based Reinforcement Learning process.
  arXiv  Detail & Related papers  (2022-11-30T17:05:22Z)
• SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction [127.43571146741984]
  Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery. wet experiments remain the most reliable method, but they are time-consuming and resource-intensive. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. We present the SSM-DTA framework, which incorporates three simple yet highly effective strategies.
  arXiv  Detail & Related papers  (2022-06-20T14:53:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.