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ChemTS: an efficient python library for de novo molecular generation

SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2017年第1期18卷 972-976页

作者： Yang, Xiufeng Zhang, Jinzhe Yoshizoe, Kazuki Terayama, Kei Tsuda, Koji Univ Tokyo Grad Sch Frontier Sci Kashiwa Chiba Japan INSA Lyon Dept Biosci Villeurbanne France RIKEN Ctr Adv Intelligence Project Tokyo Japan Natl Inst Mat Sci Tsukuba Ibaraki Japan

Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://***/tsudalab/ChemTS. [GRAPHICS] .

关键词： Molecular design Monte Carlo tree search recurrent neural network python library

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PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions

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JOURNAL OF CHEMINFORMATICS 2018年第1期10卷 16-16页

作者： Dong, Jie Yao, Zhi-Jiang Zhang, Lin Luo, Feijun Lin, Qinlu Lu, Ai-Ping Chen, Alex F. Cao, Dong-Sheng Cent S Univ Xiangya Sch Pharmaceut Sci 172 Tongzipo Rd Changsha Hunan Peoples R China Cent South Univ Forestry & Technol Coll Food Sci & Engn Natl Engn Lab Deep Proc Rice & Byprod Changsha Hunan Peoples R China Hong Kong Baptist Univ Sch Chinese Med Inst Adv Translat Med Bone & Joint Dis Hong Kong Hong Kong Peoples R China Cent S Univ Xiangya Hosp 3 Ctr Vasc Dis & Translat Med Changsha Hunan Peoples R China

Background: With the increasing development of biotechnology and informatics technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these data needs to be extracted and transformed to useful knowledge by various data mining methods. Considering the amazing rate at which data are accumulated in chemistry and biology fields, new tools that process and interpret large and complex interaction data are increasingly important. So far, there are no suitable toolkits that can effectively link the chemical and biological space in view of molecular representation. To further explore these complex data, an integrated toolkit for various molecular representation is urgently needed which could be easily integrated with data mining algorithms to start a full data analysis pipeline. Results: Herein, the python library PyBioMed is presented, which comprises functionalities for online download for various molecular objects by providing different IDs, the pretreatment of molecular structures, the computation of various molecular descriptors for chemicals, proteins, DNAs and their interactions. PyBioMed is a feature-rich and highly customized python library used for the characterization of various complex chemical and biological molecules and interaction samples. The current version of PyBioMed could calculate 775 chemical descriptors and 19 kinds of chemical fingerprints, 9920 protein descriptors based on protein sequences, more than 6000 DNA descriptors from nucleotide sequences, and interaction descriptors from pairwise samples using three different combining strategies. Several examples and five real-life applications were provided to clearly guide the users how to use PyBioMed as an integral part of data analysis projects. By using PyBioMed, users are able to start a full pipelining from getting molecular data, pretreating molecules, molecular representation to constructing machine learning models conveniently. Co

关键词： Molecular representation Molecular descriptors python library Chemoinformatics Data integration Bioinformatics

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SSLearn: A Semi-Supervised Learning library for python

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SOFTWAREX 2025年 29卷

作者： Garrido-Labrador, Jose L. Maudes-Raedo, Jesus M. Rodriguez, Juan J. Garcia-Osorio, Cesar I. Univ Burgos Escuela Politecn Super Avd Cantabria S-N Burgos 09006 Spain

SSLearn is an open-source python-based library that advances semi-supervised learning (SSL) with a focus on wrapper algorithms and restricted set classification (RSC), a novel paradigm. It fosters innovation by allowing researchers to modify methods or create new ones, facilitating access to state-of-the-art algorithms and comparative studies. As the only library incorporating RSC for constrained classification, SSLearn fills an important gap in SSL tools. Fully compatible with Scikit-Learn, it integrates seamlessly into research workflows, lowering the barrier to entry to SSL and catalyzing its adoption in diverse domains. This makes SSLearn a critical resource for advancing SSL research and applications.

关键词： Semi-supervised learning python library Self-training Co-training Restricted set classification

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python based Memristor Model library for Variability Analysis 56

Python based Memristor Model Library for Variability Analysi...

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56th IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Radhakrishnan, Aswani Babu, Sreeja James, Alex Digital Univ Kerala Veiloor Kerala India

ISBN: (纸本)9781665451093

Variability analysis is an essential step in the design of robust memristive systems. The memristor devices often are faced with device to device, and cycle to cycle variability, which can be analysed using different types of memristor model parameters. Different memristor devices fit best in behaviour to different memristor models, and there is no universal model that fits all memristor-like devices. Therefore, it is important to develop a library of memristor models to cross validate, fit and analyse the behavior of realistic devices connecting memristive systems implementation. In this paper, we propose a library that contains different models of memristor, that can be programmed, and analysed for studying variability, and sensitivity of model parameters. The focus is on those parameters that are practically relevant to the design of realistic memristive systems. This library provides the options for parameter tuning, noise analysis and device fitting to like the right models to the right devices.

关键词： Memristor model python library Variability

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PyDPLib: python Differential Privacy library for Private Medical Data Analytics

PyDPLib: Python Differential Privacy Library for Private Med...

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IEEE International Conference on Digital Health (ICDH) / IEEE World Congress on Services (SERVICES)

作者： Imtiaz, Sana Matthies, Philipp Pinto, Francisco Maros, Mate Wenz, Holger Sadre, Ramin Vlassov, Vladimir KTH Royal Inst Technol Stockholm Sweden Smart Reporting GmbH Munich Germany Heidelberg Univ Med Fac Mannheim Mannheim Germany Catholic Univ Louvain Louvain Belgium

ISBN: (纸本)9781665416856

Pharmaceutical and medical technology companies accessing real-world medical data are not interested in personally identifiable data but rather in cohort data such as statistical aggregates, patterns, and trends. These companies cooperate with medical institutions that collect medical data and want to share it but they need to protect the privacy of individuals on the shared data. We present PyDPLib, a python Differential Privacy library for private medical data analytics. We illustrate an application of differential privacy using PyDPLib in our platform for visualizing private statistics on a database of prostate cancer patients. Our experimental results show that PyDPLib allows creating statistical data plots without compromising patients' privacy while preserving underlying data distributions. Even though PyDPLib has been developed to be used in our platform for reporting the radiological examinations and procedures, it is general enough to be used to provide differential privacy on data in any data analytics and visualization platform, service or application.

关键词： Differential privacy python library private visual statistics electronic data capture prostate cancer dataset

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MetaPerceptron: A standardized framework for metaheuristic-driven multi-layer perceptron optimization

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COMPUTER STANDARDS & INTERFACES 2025年 93卷

作者： Van Thieu, Nguyen Mirjalili, Seyedali Garg, Harish Hoang, Nguyen Thanh PHENIKAA Univ Fac Comp Sci Ha Dong 12116 Hanoi Vietnam Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Australia Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary Thapar Inst Engn & Technol Deemed Univ Dept Math Patiala 147004 Punjab India Natl Econ Univ Fac Data Sci & Artificial Intelligence Hanoi Vietnam

The multi-layer perceptron (MLP) remains a foundational architecture within neural networks, widely recognized for its ability to model complex, non-linear relationships between inputs and outputs. Despite its success, MLP training processes often face challenges like susceptibility to local optima and overfitting when relying on traditional gradient descent optimization. Metaheuristic algorithms (MHAs) have recently emerged as robust alternatives for optimizing MLP training, yet no current package offers a comprehensive, standardized framework for MHA-MLP hybrid models. This paper introduces MetaPerceptron, an standardized open-source python framework designed to integrate MHAs with MLPs seamlessly, supporting both regression and classification tasks. MetaPerceptron is built on top of PyTorch, Scikit-Learn, and Mealpy. Through this design, MetaPerceptron promotes standardization in MLP optimization, incorporating essential machine learning utilities such as model forecasting, feature selection, hyperparameter tuning, and pipeline creation. By offering over 200 MHAs, MetaPerceptron empowers users to experiment across a broad array of metaheuristic optimization techniques without reimplementation. This framework significantly enhances accessibility, adaptability, and consistency in metaheuristic-trained neural network research and applications, positioning it as a valuable resource for machine learning, data science, and computational optimization. The entire source code is freely available on Github: http s://***/thieu1995/MetaPerceptron

关键词： Metaheuristic algorithms Multilayer perceptron Metaheuristic-based MLP python library Neural network Open-source software

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IntelELM: A python framework for intelligent metaheuristic-based extreme learning machine

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NEUROCOMPUTING 2025年 618卷

作者： Thieu, Nguyen Van Houssein, Essam H. Oliva, Diego Hung, Nguyen Duy PHENIKAA Univ Fac Comp Sci Hanoi 12116 Vietnam Minia Univ Fac Comp & Informat Al Minya Egypt Univ Guadalajara CUCEI Dept Ingn Electrofoton Guadalajara Mexico Artificial Intelligence Independent Res Grp Hanoi 100000 Vietnam Viettel Networks Hanoi 100000 Vietnam

This study introduces IntelELM, an open-source python library designed for hybrid neural networks that integrate Extreme Learning Machine (ELM) with Metaheuristic Algorithms (MHAs). Built on the foundations of two well-established libraries, Scikit-Learn and Mealpy, IntelELM offers four primary strategies for addressing regression and classification tasks. These strategies are implemented through the ElmRegressor and ElmClassifier classes for traditional ELM, as well as the MhaElmRegressor and MhaElmClassifier for hybrid metaheuristic-based ELM models. The library is easy to install and use, especially for individuals familiar with the Scikit-Learn ecosystem. IntelELM comprises at least 402 distinct models across these four primary classes, encompassing classical ELM regression and classification models, as well as over 200 metaheuristic-based ELM regression and classification models each. To demontrade the power of the proposed library, we evaluate several hybrid models from the IntelELM library alongside traditional machine learning models across three benchmark datasets. Experimental results demonstrate that the hybrid models within IntelELM exhibit competitive performance across various performance metrics compared to traditional machine learning approaches. These findings underscore the library's potential to offer effective solutions to real-world problems and contribute significantly to the computer science community. We have released the source code of the library as open-source, inviting the research community to conduct widespread evaluations of this comprehensive framework as a promising tool for research studies and real-world solutions. The source code can be found at https://***/thieu1995/ IntelELM.

关键词： Metaheuristic algorithms Extreme learning machine Metaheuristic optimization-based ELM Neural network python library Machine learning

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Unified Planning: Modeling, manipulating and solving AI planning problems in python

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SOFTWAREX 2025年 29卷

作者： Micheli, Andrea Bit-Monnot, Arthur Roger, Gabriele Scala, Enrico Valentini, Alessandro Framba, Luca Rovetta, Alberto Trapasso, Alessandro Bonassi, Luigi Gerevini, Alfonso Emilio Iocchi, Luca Ingrand, Felix Kockemann, Uwe Patrizi, Fabio Saetti, Alessandro Serina, Ivan Stock, Sebastian Fdn Bruno Kessler Trento Italy Univ Toulouse LAAS CNRS CNRS INSA Toulouse France Univ Basel Basel Switzerland Univ Brescia Brescia Italy Orebro Univ Orebro Sweden DFKI Osnabruck Germany Sapienza Univ Rome Rome Italy

Automated planning is a branch of artificial intelligence aiming at finding a course of action that achieves specified goals, given a description of the initial state of a system and a model of possible actions. There are plenty of planning approaches working under different assumptions and with different features (e.g. classical, temporal, and numeric planning). When automated planning is used in practice, however, the set of required features is often initially unclear. The Unified Planning (UP) library addresses this issue by providing a featurerich python API for modeling automated planning problems, which are solved seamlessly by planning engines that specify the set of features they support. Once a problem is modeled, UP can automatically find engines that can solve it, based on the features used in the model. This greatly reduces the commitment to specific planning approaches and bridges the gap between planning technology and its users.

关键词： Automated planning and scheduling python library Interoperability

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FOMCONpy: Fractional-Order Modelling and Control library for python 43

FOMCONpy: Fractional-Order Modelling and Control Library for...

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43rd International Conference on Telecommunications and Signal Processing (TSP)

作者： Onyedi, Tobechukwu Tepljakov, Aleksei Petlenkov, Eduard Tallinn Univ Technol Dept Comp Sci Tallinn Estonia

ISBN: (纸本)9781728163765

FOMCONpy is a new fractional-order modelling and control toolbox for python. It is an extension of the existing FOMCON toolbox for MATLAB, but this time aiming at python users and the Internet of Things (IoT) community. Just like the original toolbox, it offers a set of tools for researchers in the field of fractional-order control. In this paper, the current features and major modules of the library are outlined while reviewing the associated mathematical concepts. Fractional-order system analysis and model identification in the context of the toolbox are discussed. A comparison between FOMCONpy and FOMCON is also presented to show the validity of the implemented methods and algorithms. Finally, benchmark results of running FOMCONpy on some selected embedded devices Was provided.

关键词： fractional calculus python library identification internet of things control system design

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Evaluating test quality of python libraries for IoT applications at the network edge

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WIRELESS NETWORKS 2023年第7期30卷 6603页

作者： Chen, Zhifei Jia, Chiheng Chen, Lin Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China

The Internet of Things (IoT) frequently exchange data through the Internet by comprising a complex network of smart devices. However, various vulnerabilities of IoT applications are observed which shall keep IoT as a technology in danger. Therefore, IoT applications should go through rigorous testing at the network edge. python is widely used for programming IoT devices as well as developing the corresponding back-end at the network edge. Due to the difficulty of static program analysis on dynamic languages, the quality of test code in python libraries for IoT development faces a serious threat, which in turn affects the performance of IoT applications. In order to evaluate test quality of python libraries for IoT applications, we propose a test analysis framework named Pysta. It uses a hybrid analysis engine to support the multi-dimensional evaluation of python test code. Based on Pysta, we then conduct an empirical study on test quality in eight python libraries which are widely used for IoT development to investigate test code composition, test objects, test co-evolution, and test code smells. The study results reveal the major problems and challenge in the tests of python libraries for IoT applications, including low naming consistency, single form of assertion, lacking edge tests, low test coverage in large-scale projects, delaying test co-evolution, and various code smells in tests. This work can inspire researchers and developers on how to improve test quality of IoT applications and further reduce security risks associated with IoT systems.

关键词： Internet of Things Network edge Test quality python library

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