Molecular Informatics最新文献_第2页

PairMap2: A Web Application for Intermediate-Molecule Insertion in Relative Binding Free Energy Calculations. PairMap2：一个用于相对结合自由能计算中间分子插入的Web应用程序。

IF 3.1 4区医学

Molecular Informatics Pub Date : 2026-08-01 DOI: 10.1002/minf.70046

Kairi Furui, Masahito Ohue

引用次数: 0

Design of Thermotropic Liquid Crystal Molecules With a Wide Temperature Range for the Liquid Crystal Phase Using Machine Learning. 利用机器学习设计液晶相宽温度范围的热致液晶分子。

IF 3.1 4区医学

Molecular Informatics Pub Date : 2026-08-01 DOI: 10.1002/minf.70044

Naoki Masuyama, Hiromasa Kaneko

引用次数: 0

MolScope: A Transparent Representation-Aware Workflow Toolkit for Reproducible Medicinal-Chemistry Triage. MolScope：一个透明的表示感知工作流程工具包，用于可重复的药物化学分类。

IF 3.1 4区医学

Molecular Informatics Pub Date : 2026-08-01 DOI: 10.1002/minf.70048

Khaled M Elokely

引用次数: 0

Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery. 基于几何深度学习的小分子药物设计模型

IF 3.1 4区医学

Molecular Informatics Pub Date : 2026-08-01 DOI: 10.1002/minf.70047

Amit Kumar Srivastav, Unnati Modi, Rahul Kumar, Dhiraj Bhatia, Raghu Solanki

{"title":"Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.","authors":"Amit Kumar Srivastav, Unnati Modi, Rahul Kumar, Dhiraj Bhatia, Raghu Solanki","doi":"10.1002/minf.70047","DOIUrl":"10.1002/minf.70047","url":null,"abstract":"Deep neural network (DNN)-based in silico models show great promise in predicting the properties and bioactivities of novel compounds, including small molecules. Among traditional approaches, structure-based drug design (SBDD) remains a fundamental approach for drug discovery using molecular docking, scoring functions, and molecular dynamics simulations. However, these approaches are often constrained by limited flexibility, resolution, and generalizability. Geometric deep learning (GDL) offers a transformative alternative by enabling models to learn directly from non-Euclidean molecular representations, such as graphs, point clouds, and meshes, capturing critical 3D spatial relationships inherent to protein-ligand interactions. This review highlights the theoretical underpinnings and practical applications of GDL in small-molecule drug discovery, focusing on tasks including binding affinity prediction, virtual screening, de novo molecule generation, pose prediction, ADMET profiling, and protein flexibility modeling. We explore key GDL architectures, graph neural networks, SE(3)-equivariant networks, 3D convolutional neural networks, point cloud models, and geometric transformers, and assess their performance across various drug discovery benchmarks. The integration of geometry-aware AI models with experimental and computational workflows was also highlighted for its potential to streamline hit-to-lead optimization and advance rational drug design. Despite remarkable progress, the field faces challenges including limited high-quality 3D structural datasets, protein flexibility representation, and the interpretability of deep models. Addressing these issues through hybrid modeling approaches, multi-resolution learning, and self-supervised training could further elevate GDL's impact. Ultimately, GDL stands at the frontier of AI-enhanced pharmaceutical innovation, offering unprecedented precision, efficiency, and insight in the pursuit of next-generation therapeutics.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"45 8","pages":"e70047"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13454516/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148701813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Linkerability of Protein Ligands: Insights From Cocrystal Structures and Implications for DNA-Encoded Libraries. 蛋白质配体的可连接性：来自共晶结构的见解和对dna编码文库的影响。

IF 3.1 4区医学

Molecular Informatics Pub Date : 2026-08-01 DOI: 10.1002/minf.70045

Raphael M Franzini

引用次数: 0

To What Extent Can We Extrapolate Proteochemometric Models: A Case Study for the SLC6 Transporter Family. 我们可以在多大程度上推断蛋白质化学模型：一个SLC6转运蛋白家族的案例研究。

IF 3.1 4区医学

Molecular Informatics Pub Date : 2026-07-01 DOI: 10.1002/minf.70043

Uday Abu-Shehab, Gerhard Ecker

{"title":"To What Extent Can We Extrapolate Proteochemometric Models: A Case Study for the SLC6 Transporter Family.","authors":"Uday Abu-Shehab, Gerhard Ecker","doi":"10.1002/minf.70043","DOIUrl":"10.1002/minf.70043","url":null,"abstract":"Proteochemometrics (PCM) modeling combines protein and ligand information to create predictive models for biological activity. It aims to extrapolate information across targets, enabling its application in screening drug candidates across a whole family of proteins. In this study, we investigate the ability of PCM models to extrapolate information from data-rich proteins to data-poor ones and present a series of PCM models for the SLC6 transporter family showing reasonable performance (Q2 values up to 0.79). Moreover, feature importance analysis pointed towards residue position A173 in hSERT, corresponding to G149 and G153 in hNET and hDAT, respectively, to be relevant for subtype selectivity. However, examining the impact of different data splits on model validation metrics highlights potential over-optimism when only considering target stratification splits. Target stratification split only maintains the train/test ratio across all targets, preserving per target balance without considering chemical similarity. However, when performing leave-one-transporter-out studies, a considerable drop in performance was observed. This points towards the need for more complex technologies to exploit the potential of PCM and identify new drug candidates.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"45 7","pages":"e70043"},"PeriodicalIF":3.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13413613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148605323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DATTs: A Database of Disease-Associated Therapeutic Targets With Required Actions for Treatment. DATTs：疾病相关治疗靶点及治疗所需行动的数据库。

IF 3.1 4区医学

Molecular Informatics Pub Date : 2026-07-01 DOI: 10.1002/minf.70042

Ryusuke Sawada, Noriko Yuyama Otani, Michio Iwata, Tomokazu Shibata, Shujiro Okuda, Hiroyo Nishide, Ikuo Uchiyama, Yoshihiro Yamanishi

{"title":"DATTs: A Database of Disease-Associated Therapeutic Targets With Required Actions for Treatment.","authors":"Ryusuke Sawada, Noriko Yuyama Otani, Michio Iwata, Tomokazu Shibata, Shujiro Okuda, Hiroyo Nishide, Ikuo Uchiyama, Yoshihiro Yamanishi","doi":"10.1002/minf.70042","DOIUrl":"10.1002/minf.70042","url":null,"abstract":"Proteins involved in pathophysiological mechanisms are widely recognized as promising therapeutic targets for drug discovery. The therapeutic effects of drugs are primarily mediated through the inhibition or activation of target proteins. Therefore, beyond identifying the appropriate molecular target, determining the direction of action on the target protein is a critical factor that shapes the efficacy and characteristics of a treatment. In this paper, we present DATTs (Disease-Associated Therapeutic Targets), which is a new database of therapeutic protein targets with required actions for medical treatment. The information is extracted from public documents such as scientific papers and medical textbooks. The available information includes disease-target protein associations and the actions of drugs on these proteins required for therapeutic effects (e.g., activation, inhibition, or targeting). The DATTs currently includes information on 613 diseases, 1475 therapeutic target proteins, and 7415 disease-protein associations. Additional information such as links to other databases containing data on the molecular functions of target proteins and classification of diseases is also accessible. The DATTs can provide useful clues for investigation of disease mechanisms, novel drug design, and drug repositioning. The DATTs is accessible at https://datts.nibb.ac.jp/.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"45 7","pages":"e70042"},"PeriodicalIF":3.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13413248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148605365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Undersampling Techniques for Nonlinear Chemical Space Visualization. 非线性化学空间可视化欠采样技术。

IF 3.1 4区医学

Molecular Informatics Pub Date : 2026-07-01 DOI: 10.1002/minf.70041

Akash Surendran, Krisztina Zsigmond, Ramón Alain Miranda-Quintana

引用次数: 0

SpaceExpander: An Automated System for Drafting Markush Claims to Expand Chemical Space. 太空扩张器：一个自动起草马库什声明以扩大化学空间的系统。

IF 3.1 4区医学

Molecular Informatics Pub Date : 2026-06-01 DOI: 10.1002/minf.70040

Rui Wu, Liyun Mao, Yanyan Diao, Honglin Li

引用次数: 0

ConGen: Targeted Molecule Generation Through Contrastive Learning and Latent Optimization. ConGen：通过对比学习和潜在优化的目标分子生成。

IF 3.1 4区医学

Molecular Informatics Pub Date : 2026-05-01 DOI: 10.1002/minf.70038

Can Koban, Gökçe Uludoğan, Elif Ozkirimli, Arzucan Özgür

{"title":"ConGen: Targeted Molecule Generation Through Contrastive Learning and Latent Optimization.","authors":"Can Koban, Gökçe Uludoğan, Elif Ozkirimli, Arzucan Özgür","doi":"10.1002/minf.70038","DOIUrl":"10.1002/minf.70038","url":null,"abstract":"The discovery of novel compounds for protein targets is an important step in the drug discovery process. Target-specific molecules can be designed based on protein structures, but molecule prediction based on protein sequences alone remains a challenge. We hypothesize that including information on noninteracting pairs can improve the specificity and generalizability of molecule prediction models. To this end, we introduce ConGen, a sequence-conditioned framework for targeted molecule generation from protein sequence alone. To our knowledge, it is the first model that combines contrastive protein-molecule representation learning with interacting and noninteracting molecules and latent optimization (LO) in a shared pretrained space, addressing the limitations of interacting-only training. ConGen follows two stages: (i) contrastive learning (CL) maps proteins and molecules into a joint space where interacting pairs are pulled together and noninteracting pairs are pushed apart and (ii) LO starts from random molecule latents, moves them toward the target protein's embedding, and decodes a local neighborhood to propose candidates. This approach enables the generation of molecules specifically designed for a given target by efficiently exploiting the structure of the shared latent space. Our results show that ConGen achieves performance comparable to EncDecLM, a state-of-the-art sequence-based model for targeted drug generation. An ablation study further demonstrates the contributions of both the CL and LO stages.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":"45 5","pages":"e70038"},"PeriodicalIF":3.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147974065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0