Cover Image

Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Adamczyk, Jakub, Ludynia, Piotr
Format:	Preprint
Published:	2024
Subjects:	Software Engineering Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Python library for efficient computation of molecular fingerprints
by: Szafarczyk, Michał, et al.
Published: (2024)

Molecular Fingerprints Are Strong Models for Peptide Function Prediction
by: Adamczyk, Jakub, et al.
Published: (2025)

evomap: A Toolbox for Dynamic Mapping in Python
by: Matthe, Maximilian
Published: (2025)

Analysing Python Machine Learning Notebooks with Moose
by: Mignard, Marius, et al.
Published: (2025)

PyPulse: A Python Library for Biosignal Imputation
by: Gao, Kevin, et al.
Published: (2024)

MLXP: A Framework for Conducting Replicable Experiments in Python
by: Arbel, Michael, et al.
Published: (2024)

regAL: Python Package for Active Learning of Regression Problems
by: Surzhikova, Elizaveta, et al.
Published: (2024)

DocuMint: Docstring Generation for Python using Small Language Models
by: Poudel, Bibek, et al.
Published: (2024)

DomainLab: A modular Python package for domain generalization in deep learning
by: Sun, Xudong, et al.
Published: (2024)

Can LLMs Find Bugs in Code? An Evaluation from Beginner Errors to Security Vulnerabilities in Python and C++
by: Mhatre, Akshay, et al.
Published: (2025)

Practical programming research of Linear DML model based on the simplest Python code: From the standpoint of novice researchers
by: Yao, Shunxin
Published: (2025)

Towards a Neural Debugger for Python
by: Beck, Maximilian, et al.
Published: (2026)

CigaR: Cost-efficient Program Repair with LLMs
by: Hidvégi, Dávid, et al.
Published: (2024)

An Empirical Evaluation of Locally Deployed LLMs for Bug Detection in Python Code
by: Vulićević, Jelena Ilić
Published: (2026)

scicode-lint: Detecting Methodology Bugs in Scientific Python Code with LLM-Generated Patterns
by: Samsonau, Sergey V.
Published: (2026)

OpenClassGen: A Large-Scale Corpus of Real-World Python Classes for LLM Research
by: Rahman, Musfiqur, et al.
Published: (2025)

evclust: Python library for evidential clustering
by: Soubeiga, Armel, et al.
Published: (2025)

HaPy-Bug -- Human Annotated Python Bug Resolution Dataset
by: Przymus, Piotr, et al.
Published: (2025)

Who Wins the Race? (R Vs Python) - An Exploratory Study on Energy Consumption of Machine Learning Algorithms
by: Chattaraj, Rajrupa, et al.
Published: (2025)

Functional Programming Paradigm of Python for Scientific Computation Pipeline Integration
by: Zhang, Chen, et al.
Published: (2024)

mcdok at SemEval-2026 Task 13: Finetuning LLMs for Detection of Machine-Generated Code
by: Skurla, Adam, et al.
Published: (2026)

Efficient Solvers for SLOPE in R, Python, Julia, and C++
by: Larsson, Johan, et al.
Published: (2025)

ModelVerification.jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks
by: Wei, Tianhao, et al.
Published: (2024)

A Survey on LLM-based Code Generation for Low-Resource and Domain-Specific Programming Languages
by: Joel, Sathvik, et al.
Published: (2024)

Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures
by: Alizadeh, Negar, et al.
Published: (2024)

GPT Store Mining and Analysis
by: Su, Dongxun, et al.
Published: (2024)

An Effective Software Risk Prediction Management Analysis of Data Using Machine Learning and Data Mining Method
by: Xu, Jinxin, et al.
Published: (2024)

The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design Trade-Offs in Ensemble Learning Systems
by: Omar, Rafiullah, et al.
Published: (2024)

Trained Without My Consent: Detecting Code Inclusion In Language Models Trained on Code
by: Majdinasab, Vahid, et al.
Published: (2024)

Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs
by: Shan, Shiwen, et al.
Published: (2024)

An Exploratory Investigation into Code License Infringements in Large Language Model Training Datasets
by: Katzy, Jonathan, et al.
Published: (2024)

Deep Learning and Machine Learning: Advancing Big Data Analytics and Management with Design Patterns
by: Chen, Keyu, et al.
Published: (2024)

RepairBench: Leaderboard of Frontier Models for Program Repair
by: Silva, André, et al.
Published: (2024)

Towards MLOps: A DevOps Tools Recommender System for Machine Learning System
by: Shah, Pir Sami Ullah, et al.
Published: (2024)

Automating REST API Postman Test Cases Using LLM
by: Sri, S Deepika, et al.
Published: (2024)

Mining Action Rules for Defect Reduction Planning
by: Oueslati, Khouloud, et al.
Published: (2024)

Refining Joint Text and Source Code Embeddings for Retrieval Task with Parameter-Efficient Fine-Tuning
by: Galliamov, Karim, et al.
Published: (2024)

Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Products
by: Nahar, Nadia, et al.
Published: (2024)

InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models
by: Li, Linyi, et al.
Published: (2024)

How to Sustainably Monitor ML-Enabled Systems? Accuracy and Energy Efficiency Tradeoffs in Concept Drift Detection
by: Omar, Rafiullah, et al.
Published: (2024)