Emerging Opportunities of Using Large Language Models for Translation Between Drug Molecules and Indications

• URL: http://arxiv.org/abs/2402.09588v2
• Date: Fri, 16 Feb 2024 20:55:08 GMT
• Title: Emerging Opportunities of Using Large Language Models for Translation Between Drug Molecules and Indications
• Authors: David Oniani, Jordan Hilsman, Chengxi Zang, Junmei Wang, Lianjin Cai, Jan Zawala, Yanshan Wang
• Abstract summary: We propose a new task, which is the translation between drug molecules and corresponding indications. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases.
• Score: 6.832024637226738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A drug molecule is a substance that changes the organism's mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translating between molecules and their textual descriptions, there remains a gap in research regarding their application in facilitating the translation between drug molecules and indications, or vice versa, which could greatly benefit the drug discovery process. The capability of generating a drug from a given indication would allow for the discovery of drugs targeting specific diseases or targets and ultimately provide patients with better treatments. In this paper, we first propose a new task, which is the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task. Specifically, we consider nine variations of the T5 LLM and evaluate them on two public datasets obtained from ChEMBL and DrugBank. Our experiments show the early results of using LLMs for this task and provide a perspective on the state-of-the-art. We also emphasize the current limitations and discuss future work that has the potential to improve the performance on this task. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases and significantly reduce the cost of drug discovery, with the potential to revolutionize the field of drug discovery in the era of generative AI.

Related papers

• Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models [46.05020842978823]
  Large Language Models (LLMs) have emerged as powerful tools to navigate this complex data landscape. RAGGED is a comprehensive workflow designed to support investigators with knowledge integration and hypothesis generation.
  arXiv  Detail & Related papers  (2024-07-17T07:44:18Z)
• DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing [4.969453745531116]
  DrugCLIP is a contrastive learning method to learn drug and disease's interaction without negative labels. We have curated a drug repurposing dataset based on real-world clinical trial records.
  arXiv  Detail & Related papers  (2024-07-02T13:41:59Z)
• Large Language Model Distilling Medication Recommendation Model [61.89754499292561]
  We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs) Our research aims to transform existing medication recommendation methodologies using LLMs. 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)
• NeuroCADR: Drug Repurposing to Reveal Novel Anti-Epileptic Drug Candidates Through an Integrated Computational Approach [0.0]
  Drug repurposing is an emerging approach for drug discovery involving the reassignment of existing drugs for novel purposes. A proposed algorithm is NeuroCADR, a novel system for drug repurposing via a multi-pronged approach consisting of k-nearest neighbor algorithms (KNN), random forest classification, and decision trees. Data was sourced from several databases consisting of interactions between diseases, symptoms, genes, and affiliated drug molecules, which were then compiled into datasets expressed in binary. NeuroCADR identified novel drug candidates for epilepsy that can be further approved through clinical trials.
  arXiv  Detail & Related papers  (2023-09-04T03:21:43Z)
• Artificial Intelligence for Drug Discovery: Are We There Yet? [0.08306867559432653]
  Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small molecule drugs.
  arXiv  Detail & Related papers  (2023-07-13T01:51:26Z)
• SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design [64.69434941796904]
  We propose a novel setting and models for in-context drug synergy learning. We are given a small "personalized dataset" of 10-20 drug synergy relationships in the context of specific cancer cell targets. Our goal is to predict additional drug synergy relationships in that context.
  arXiv  Detail & Related papers  (2023-06-19T17:03:46Z)
• Knowledge-Driven New Drug Recommendation [88.35607943144261]
  We develop a drug-dependent multi-phenotype few-shot learner to bridge the gap between existing and new drugs. EDGE eliminates the false-negative supervision signal using an external drug-disease knowledge base. Results show that EDGE achieves 7.3% improvement on the ROC-AUC score over the best baseline.
  arXiv  Detail & Related papers  (2022-10-11T16:07:52Z)
• 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)
• MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning [61.74958429818077]
  MolDesigner is a human-in-the-loop web user-interface (UI) for drug developers. A developer can draw a drug molecule in the interface. In the backend, more than 17 state-of-the-art DL models generate predictions on important indices that are crucial for a drug's efficacy.
  arXiv  Detail & Related papers  (2020-10-05T21:25:25Z)
• Semi-Supervised Hierarchical Drug Embedding in Hyperbolic Space [19.73556079390888]
  A drug hierarchy is a valuable source that encodes human knowledge of drug relations in a tree-like structure. Here, we develop a semi-supervised drug embedding that incorporates two sources of information. We show that the learned drug embedding can be used to find new uses for existing drugs and to discover side-effects.
  arXiv  Detail & Related papers  (2020-06-01T14:46:31Z)

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.