**ChemLint ** — cheminformatics 및 분자 ML 도구에 LLMs 네이티브 액세스를 제공하는 MCP 서버

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요약

ChemLint는 강력한 분자 기계 학습 workbenches로 AI 조수를 변형시킵니다. Model Context Protocol (MCP)을 통해 LLMs는 분자 구조를 조작하는 기능을 얻고, descriptors, 기차 ML 모델을 계산하고, 종합 분석 보고서를 생성합니다. - 자연 대화를 통해.

Claude Desktop (또는 MCP 클라이언트)과 채팅하여 일반적으로 Python 스크립트를 작성하고 여러 라이브러리를 juggling해야합니다.

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✨ 주요 특징

분자 작업

• SMILES 처리: 표준화, 정형화, 검증, 10단계 청소 파이프라인 -Molecular Descriptors: 단순(MW, LogP, TPSA) 및 복합(ECFP, MACCS, RDKit 지문)
• Scaffold Analysis: Bemis-Murcko, 일반적인 비계, 다양성 미터를 가진 순환 골격
• Similarity & Clustering: Tanimoto 유사성, DBSCAN, 계층, k-means, Butina 클러스터링
• Substructure Matching: 88+ 내장 기능 그룹과 SMARTS 패턴 감지

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🧬 ChemLint

ChemLint — An MCP server that gives LLMs native access to cheminformatics and molecular ML tools

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Summary

ChemLint transforms AI assistants into powerful molecular machine learning workbenches. Through the Model Context Protocol (MCP), LLMs gain the ability to manipulate molecular structures, calculate descriptors, train ML models, and generate comprehensive analysis reports — all through natural conversation.

Simply chat with Claude Desktop (or any MCP client) to perform complex cheminformatics workflows that would normally require writing Python scripts and juggling multiple libraries.

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✨ Key Features

🧪 Molecular Operations

• SMILES Processing: Standardization, canonicalization, validation, 10-step cleaning pipelines
• Molecular Descriptors: Simple (MW, LogP, TPSA) and complex (ECFP, MACCS, RDKit fingerprints)
• Scaffold Analysis: Bemis-Murcko, generic scaffolds, cyclic skeletons with diversity metrics
• Similarity & Clustering: Tanimoto similarity, DBSCAN, hierarchical, k-means, Butina clustering
• Substructure Matching: SMARTS pattern detection with 88+ built-in functional groups

🤖 Machine Learning

• 33 ML Algorithms: Classification & regression (RF, GBM, SVM, linear models, ensembles with uncertainty)
• Cross-Validation: 6 strategies (k-fold, stratified, Monte Carlo, scaffold, cluster, leave-P-out)
• Hyperparameter Tuning: Grid search, random search with customizable parameter spaces
• Model Evaluation: 20+ metrics, confusion matrices, ROC curves, calibration plots

📊 Quality Reports

• Data Quality Analysis: 19-section comprehensive report (PAINS, Lipinski, duplicates, stereochemistry, etc.)
• Split Quality Analysis: 8 data leakage checks (duplicates, similarity, scaffolds, stereoisomers)
• Scaffold Reports: Diversity metrics (Gini, Shannon entropy), enrichment analysis, structural outliers

📈 Visualization & Statistics

• Interactive Plots: Scatter plots with molecular tooltips, histograms, density plots, box plots, heatmaps
• Statistical Tests: 15+ tests (t-test, ANOVA, Mann-Whitney, Kruskal-Wallis, chi-square, normality tests)
• Dimensionality Reduction: PCA, t-SNE for chemical space visualization
• Outlier Detection: Z-score, IQR, isolation forest, local outlier factor

🔬 Advanced Features

• Activity Cliff Detection: Find structurally similar molecules with large activity differences
• Data Splitting: Random, stratified, scaffold-based, cluster-based, temporal splits
• Duplicate Handling: Activity conflict detection with aggregation strategies