AspirinCode 的倉庫

Python source code for 3D/MI/QSAR models

最近提交 2020年12月16日

Ready-to-go Jupyter notebook for plotting AlphaFold-generated MSAs, per-residue pLDDT, and PAE.

最近提交 2024年3月26日

A free and collaborative space for Machine Learning applied to Biology

最近提交 2023年6月25日

最近提交 2025年6月2日

Application of deep generative model discovers novel and diverse functional peptides against microbial resistance

最近提交 2022年12月22日

AARON 1.0, An Automated Reaction Optimizer for New catalysts

最近提交 2021年1月12日

AlphaPPIMI: A Comprehensive Deep Learning Framework for Predicting PPI-Modulator Interactions

最近提交 2025年8月29日

Exploring the conformational ensembles of protein-protein complexes with transformer-based generative neural networks

最近提交 2024年6月9日

Feature map and function annotation of Proteins

最近提交 2023年6月21日

Config files for my GitHub profile.

最近提交 2025年8月1日

Bioinformatics And Health AI （BIHAI）

最近提交 2025年3月21日

A growing collection of bioinformatics tutorials, project guides, tools, and articles I’ve developed to make bioinformatics more accessible.

最近提交 2025年9月2日

CBKH: The Cornell Biomedical Knowledge Hub

最近提交 2021年4月13日

Quantum computing is one the most promising new trends in information processing. In this course, we will introduce from scratch the basic concepts of the quantum circuit model (qubits, gates and measures) and use them to study some of the most important quantum algorithms and protocols, including those that can be implemented with a few qubits (BB84, quantum teleportation, superdense coding...) as well as those that require multi-qubit systems (Deutsch-Jozsa, Grover, Shor..). We will also cover some of the most recent applications of quantum computing in the fields of optimization and simulation (with special emphasis on the use of quantum annealing, the quantum approximate optimization algorithm and the variational quantum eigensolver) and quantum machine learning (for instance, through the use of quantum support vector machines and quantum variational classifiers). We will also give examples of how these techniques can be used in chemistry simulations and high energy physics problems. The focus of the course will be on the practical aspects of quantum computing and on the implementation of algorithms in quantum simulators and actual quantum computers (as the ones available on the IBM Quantum Experience and D-Wave Leap). No previous knowledge of quantum physics is required and, from the mathematical point of view, only a good command of basic linear algebra is assumed. Some familiarity with the python programming language would be helpful, but is not required either.

最近提交 2021年1月6日

:test_tube: Learning Neural Generative Dynamics for Molecular Conformation Generation (ICLR 2021)

最近提交 2021年3月3日

CheTo - Chemical Topic Modeling

最近提交 2017年8月10日

CmhAttCPI: a bidirectional interpretable compound-protein interaction prediction framework based on cross attention

最近提交 2024年3月4日

A curated list of awesome computational cryo-EM methods.

最近提交 2021年3月21日

decimer.ai

最近提交 2021年1月7日

DECIMER 1.0: Deep Learning for Chemical Image Recognition using Transformers

最近提交 2021年4月29日