Digital discovery最新文献_第10页

ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning† ProtAgents：通过结合物理学和机器学习的大型语言模型多代理协作发现蛋白质

IF 6.2

Digital discovery Pub Date : 2024-05-17 DOI: 10.1039/D4DD00013G

Alireza Ghafarollahi and Markus J. Buehler

{"title":"ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning†","authors":"Alireza Ghafarollahi and Markus J. Buehler","doi":"10.1039/D4DD00013G","DOIUrl":"10.1039/D4DD00013G","url":null,"abstract":"Designing de novo proteins beyond those found in nature holds significant promise for advancements in both scientific and engineering applications. Current methodologies for protein design often rely on AI-based models, such as surrogate models that address end-to-end problems by linking protein structure to material properties or vice versa. However, these models frequently focus on specific material objectives or structural properties, limiting their flexibility when incorporating out-of-domain knowledge into the design process or comprehensive data analysis is required. In this study, we introduce ProtAgents, a platform for de novo protein design based on Large Language Models (LLMs), where multiple AI agents with distinct capabilities collaboratively address complex tasks within a dynamic environment. The versatility in agent development allows for expertise in diverse domains, including knowledge retrieval, protein structure analysis, physics-based simulations, and results analysis. The dynamic collaboration between agents, empowered by LLMs, provides a versatile approach to tackling protein design and analysis problems, as demonstrated through diverse examples in this study. The problems of interest encompass designing new proteins, analyzing protein structures and obtaining new first-principles data – natural vibrational frequencies – via physics simulations. The concerted effort of the system allows for powerful automated and synergistic design of de novo proteins with targeted mechanical properties. The flexibility in designing the agents, on one hand, and their capacity in autonomous collaboration through the dynamic LLM-based multi-agent environment on the other hand, unleashes great potentials of LLMs in addressing multi-objective materials problems and opens up new avenues for autonomous materials discovery and design.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00013g?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141059333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mining patents with large language models elucidates the chemical function landscape† 利用大型语言模型挖掘专利，阐明化学功能格局

Digital discovery Pub Date : 2024-05-07 DOI: 10.1039/D4DD00011K

Clayton W. Kosonocky, Claus O. Wilke, Edward M. Marcotte and Andrew D. Ellington

{"title":"Mining patents with large language models elucidates the chemical function landscape†","authors":"Clayton W. Kosonocky, Claus O. Wilke, Edward M. Marcotte and Andrew D. Ellington","doi":"10.1039/D4DD00011K","DOIUrl":"10.1039/D4DD00011K","url":null,"abstract":"The fundamental goal of small molecule discovery is to generate chemicals with target functionality. While this often proceeds through structure-based methods, we set out to investigate the practicality of methods that leverage the extensive corpus of chemical literature. We hypothesize that a sufficiently large text-derived chemical function dataset would mirror the actual landscape of chemical functionality. Such a landscape would implicitly capture complex physical and biological interactions given that chemical function arises from both a molecule's structure and its interacting partners. To evaluate this hypothesis, we built a Chemical Function (CheF) dataset of patent-derived functional labels. This dataset, comprising 631 K molecule–function pairs, was created using an LLM- and embedding-based method to obtain 1.5 K unique functional labels for approximately 100 K randomly selected molecules from their corresponding 188 K unique patents. We carry out a series of analyses demonstrating that the CheF dataset contains a semantically coherent textual representation of the functional landscape congruent with chemical structural relationships, thus approximating the actual chemical function landscape. We then demonstrate through several examples that this text-based functional landscape can be leveraged to identify drugs with target functionality using a model able to predict functional profiles from structure alone. We believe that functional label-guided molecular discovery may serve as an alternative approach to traditional structure-based methods in the pursuit of designing novel functional molecules.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00011k?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140881812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

iSIM: instant similarity† iSIM：即时相似性

Digital discovery Pub Date : 2024-05-07 DOI: 10.1039/D4DD00041B

Kenneth López-Pérez, Taewon D. Kim and Ramón Alain Miranda-Quintana

引用次数: 0

Correction: Predicting small molecules solubility on endpoint devices using deep ensemble neural networks 更正：利用深度集合神经网络预测小分子在终端设备上的溶解度

Digital discovery Pub Date : 2024-05-03 DOI: 10.1039/D4DD90020K

Mayk Caldas Ramos and Andrew D. White

引用次数: 0

A reproducibility study of atomistic line graph neural networks for materials property prediction† 用于材料性能预测的原子线图神经网络重现性研究

Digital discovery Pub Date : 2024-04-30 DOI: 10.1039/D4DD00064A

Kangming Li, Brian DeCost, Kamal Choudhary and Jason Hattrick-Simpers

{"title":"A reproducibility study of atomistic line graph neural networks for materials property prediction†","authors":"Kangming Li, Brian DeCost, Kamal Choudhary and Jason Hattrick-Simpers","doi":"10.1039/D4DD00064A","DOIUrl":"10.1039/D4DD00064A","url":null,"abstract":"Use of machine learning has been increasingly popular in materials science as data-driven materials discovery is becoming the new paradigm. Reproducibility of findings is paramount for promoting transparency and accountability in research and building trust in the scientific community. Here we conduct a reproducibility analysis of the work by K. Choudhary and B. Brian [npj Comput. Mater., 7, 2021, 185], in which a new graph neural network architecture was developed with improved performance on multiple atomistic prediction tasks. We examine the reproducibility for the model performance on 29 regression tasks and for an ablation analysis of the graph neural network layers. We find that the reproduced results generally exhibit a good quantitative agreement with the initial study, despite minor disparities in model performance and training efficiency that may be resulting from factors such as hardware difference and stochasticity involved in model training and data splits. The ease of conducting these reproducibility experiments confirms the great benefits of open data and code practices to which the initial work adhered. We also discuss some further enhancements in reproducible practices such as code and data archiving and providing data identifiers used in dataset splits.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00064a?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accelerated screening of gas diffusion electrodes for carbon dioxide reduction† 加速筛选用于二氧化碳还原的气体扩散电极

Digital discovery Pub Date : 2024-04-30 DOI: 10.1039/D4DD00061G

Ryan J. R. Jones, Yungchieh Lai, Dan Guevarra, Kevin Kan, Joel A. Haber and John M. Gregoire

{"title":"Accelerated screening of gas diffusion electrodes for carbon dioxide reduction†","authors":"Ryan J. R. Jones, Yungchieh Lai, Dan Guevarra, Kevin Kan, Joel A. Haber and John M. Gregoire","doi":"10.1039/D4DD00061G","DOIUrl":"10.1039/D4DD00061G","url":null,"abstract":"The electrochemical conversion of carbon dioxide to chemicals and fuels is expected to be a key sustainability technology. Electrochemical carbon dioxide reduction technologies are challenged by several factors, including the limited solubility of carbon dioxide in aqueous electrolyte as well as the difficulty in utilizing polymer electrolytes. These considerations have driven system designs to incorporate gas diffusion electrodes (GDEs) to bring the electrocatalyst in contact with both a gaseous reactant/product stream as well as a liquid electrolyte. GDE optimization typically results from manual tuning by select experts. Automated preparation and operation of GDE cells could be a watershed for the systematic study of, and ultimately the development of a materials acceleration platform (MAP) for, catalyst discovery and system optimization. Toward this end, we present the automated GDE (AutoGDE) testing system. Given a catalyst-coated GDE, AutoGDE automates the insertion of the GDE into an electrochemical cell, the liquid and gas handling, the quantification of gaseous reaction products via online mass spectroscopy, and the archiving of the liquid electrolyte for subsequent analysis.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00061g?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards equilibrium molecular conformation generation with GFlowNets† 利用 GFlowNets 生成平衡分子构象

Digital discovery Pub Date : 2024-04-29 DOI: 10.1039/D4DD00023D

Alexandra Volokhova, Michał Koziarski, Alex Hernández-García, Cheng-Hao Liu, Santiago Miret, Pablo Lemos, Luca Thiede, Zichao Yan, Alán Aspuru-Guzik and Yoshua Bengio

引用次数: 0

A versatile optimization framework for porous electrode design† 多孔电极设计的多功能优化框架

IF 6.2

Digital discovery Pub Date : 2024-04-25 DOI: 10.1039/D3DD00247K

Maxime van der Heijden, Gabor Szendrei, Victor de Haas and Antoni Forner-Cuenca

{"title":"A versatile optimization framework for porous electrode design†","authors":"Maxime van der Heijden, Gabor Szendrei, Victor de Haas and Antoni Forner-Cuenca","doi":"10.1039/D3DD00247K","DOIUrl":"10.1039/D3DD00247K","url":null,"abstract":"Porous electrodes are performance-defining components in electrochemical devices, such as redox flow batteries, as they govern the electrochemical performance and pumping demands of the reactor. Yet, conventional porous electrodes used in redox flow batteries are not tailored to sustain convection-enhanced electrochemical reactions. Thus, there is a need for electrode optimization to enhance the system performance. In this work, we present an optimization framework to carry out the bottom-up design of porous electrodes by coupling a genetic algorithm with a pore network modeling framework. We introduce geometrical versatility by adding a pore merging and splitting function, study the impact of various optimization parameters, geometrical definitions, and objective functions, and incorporate conventional electrode and flow field designs. Moreover, we show the need for optimizing geometries for specific reactor architectures and operating conditions to design next-generation electrodes, by analyzing the genetic algorithm optimization for initial starting geometries with diverse morphologies (cubic and a tomography-extracted commercial electrode), flow field designs (flow-through and interdigitated), and redox chemistries (VO2+/VO2+ and TEMPO/TEMPO+). We found that for kinetically sluggish electrolytes with high ionic conductivity, electrodes with numerous small pores and high internal surface area provide enhanced performance, whereas for kinetically facile electrolytes with low ionic conductivity, low through-plane tortuosity and high hydraulic conductance are desired. The computational tool developed in this work can further expanded to the design of high-performance electrode materials for a broad range of operating conditions, electrolyte chemistries, reactor designs, and electrochemical technologies.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d3dd00247k?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140803034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Molecular graph transformer: stepping beyond ALIGNN into long-range interactions† 分子图转换器：超越 ALIGNN，进入远距离相互作用

Digital discovery Pub Date : 2024-04-23 DOI: 10.1039/D4DD00014E

Marco Anselmi, Greg Slabaugh, Rachel Crespo-Otero and Devis Di Tommaso

引用次数: 0

Learning peptide properties with positive examples only 只用正面例子学习多肽特性

Digital discovery Pub Date : 2024-04-19 DOI: 10.1039/D3DD00218G

Mehrad Ansari and Andrew D. White

{"title":"Learning peptide properties with positive examples only","authors":"Mehrad Ansari and Andrew D. White","doi":"10.1039/D3DD00218G","DOIUrl":"10.1039/D3DD00218G","url":null,"abstract":"Deep learning can create accurate predictive models by exploiting existing large-scale experimental data, and guide the design of molecules. However, a major barrier is the requirement of both positive and negative examples in the classical supervised learning frameworks. Notably, most peptide databases come with missing information and low number of observations on negative examples, as such sequences are hard to obtain using high-throughput screening methods. To address this challenge, we solely exploit the limited known positive examples in a semi-supervised setting, and discover peptide sequences that are likely to map to certain antimicrobial properties via positive-unlabeled learning (PU). In particular, we use the two learning strategies of adapting base classifier and reliable negative identification to build deep learning models for inferring solubility, hemolysis, binding against SHP-2, and non-fouling activity of peptides, given their sequence. We evaluate the predictive performance of our PU learning method and show that by only using the positive data, it can achieve competitive performance when compared with the classical positive–negative (PN) classification approach, where there is access to both positive and negative examples.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d3dd00218g?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140629401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0