SHREC 2021: Retrieval and classification of protein surfaces equipped with physical and chemical properties
원문 초록
This paper presents the methods that have participated in the SHREC 2021 contest on retrieval and classification of protein surfaces on the basis of their geometry and physicochemical properties. The goal of the contest is to assess the capability of different computational approaches to identify different conformations of the same protein, or the presence of common sub-parts, starting from a set of molecular surfaces. We addressed two problems: defining the similarity solely based on the surface geometry or with the inclusion of physicochemical information, such as electrostatic potential, amino acid hydrophobicity, and the presence of hydrogen bond donors and acceptors. Retrieval and classification performances, with respect to the single protein or the existence of common sub-sequences, are analysed according to a number of information retrieval indicators.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
JointRank: Rank Large Set with Single Pass
From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents
Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion
From Token to Action: State Machine Reasoning to Mitigate Overthinking in Information Retrieval
Retrieval Augmented Generation based Large Language Models for Causality Mining
토론
이 논문과 연결된 포럼 글이 아직 없습니다.