Cloud-Based Real-Time Molecular Screening Platform with MolFormer

Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference) Pub Date : 2022-08-13 DOI:10.48550/arXiv.2208.06665

Brian M. Belgodere, V. Chenthamarakshan, Payel Das, Pierre L. Dognin, Toby Kurien, Igor Melnyk, Youssef Mroueh, Inkit Padhi, Mattia Rigotti, Jarret Ross, Yair Schiff, R. Young

{"title":"Cloud-Based Real-Time Molecular Screening Platform with MolFormer","authors":"Brian M. Belgodere, V. Chenthamarakshan, Payel Das, Pierre L. Dognin, Toby Kurien, Igor Melnyk, Youssef Mroueh, Inkit Padhi, Mattia Rigotti, Jarret Ross, Yair Schiff, R. Young","doi":"10.48550/arXiv.2208.06665","DOIUrl":null,"url":null,"abstract":"With the prospect of automating a number of chemical tasks with high fidelity, chemical language processing models are emerging at a rapid speed. Here, we present a cloud-based real-time platform that allows users to virtually screen molecules of interest. For this purpose, molecular embeddings inferred from a recently proposed large chemical language model, named MolFormer, are leveraged. The platform currently supports three tasks: nearest neighbor retrieval, chemical space visualization, and property prediction. Based on the functionalities of this platform and results obtained, we believe that such a platform can play a pivotal role in automating chemistry and chemical engineering research, as well as assist in drug discovery and material design tasks. A demo of our platform is provided at \\url{www.ibm.biz/molecular_demo}.","PeriodicalId":74091,"journal":{"name":"Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference)","volume":"30 1","pages":"641-644"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48550/arXiv.2208.06665","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

Abstract

With the prospect of automating a number of chemical tasks with high fidelity, chemical language processing models are emerging at a rapid speed. Here, we present a cloud-based real-time platform that allows users to virtually screen molecules of interest. For this purpose, molecular embeddings inferred from a recently proposed large chemical language model, named MolFormer, are leveraged. The platform currently supports three tasks: nearest neighbor retrieval, chemical space visualization, and property prediction. Based on the functionalities of this platform and results obtained, we believe that such a platform can play a pivotal role in automating chemistry and chemical engineering research, as well as assist in drug discovery and material design tasks. A demo of our platform is provided at \url{www.ibm.biz/molecular_demo}.

查看原文本刊更多论文

基于云的MolFormer实时分子筛选平台

随着许多化学任务的高保真自动化的前景，化学语言处理模型正在迅速出现。在这里，我们提出了一个基于云的实时平台，允许用户虚拟筛选感兴趣的分子。为此，我们利用了从最近提出的大型化学语言模型MolFormer中推断出的分子嵌入。该平台目前支持三个任务:最近邻检索、化学空间可视化和属性预测。基于该平台的功能和所获得的结果，我们认为该平台可以在自动化化学和化学工程研究中发挥关键作用，并协助药物发现和材料设计任务。我们的平台的演示在\url{www.ibm.biz/molecular_demo}上提供。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference)

自引率

0.00%

发文量