Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python
원문 초록
In this work, we present scikit-fingerprints, a Python package for computation of molecular fingerprints for applications in chemoinformatics. Our library offers an industry-standard scikit-learn interface, allowing intuitive usage and easy integration with machine learning pipelines. It is also highly optimized, featuring parallel computation that enables efficient processing of large molecular datasets. Currently, scikit-fingerprints stands as the most feature-rich library in the open source Python ecosystem, offering over 30 molecular fingerprints. Our library simplifies chemoinformatics tasks based on molecular fingerprints, including molecular property prediction and virtual screening. It is also flexible, highly efficient, and fully open source.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Descriptor-based Foundation Models for Molecular Property Prediction
Robust Molecular Property Prediction via Densifying Scarce Labeled Data
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
Impact of SMILES Notational Inconsistencies on Chemical Language Model Performance
Towards Faster and More Compact Foundation Models for Molecular Property Prediction
Learning Hierarchical Interaction for Accurate Molecular Property Prediction
토론
이 논문과 연결된 포럼 글이 아직 없습니다.