AspirinCode repositories

Python source code for 3D/MI/QSAR models

Last commit Dec 16, 2020

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

Last commit Mar 26, 2024

A free and collaborative space for Machine Learning applied to Biology

Last commit Jun 25, 2023

Last commit Jun 2, 2025

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

Last commit Dec 22, 2022

AARON 1.0, An Automated Reaction Optimizer for New catalysts

Last commit Jan 12, 2021

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

Last commit Aug 29, 2025

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

Last commit Jun 9, 2024

Feature map and function annotation of Proteins

Last commit Jun 21, 2023

Config files for my GitHub profile.

Last commit Aug 1, 2025

Bioinformatics And Health AI （BIHAI）

Last commit Mar 21, 2025

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

Last commit Sep 2, 2025

CBKH: The Cornell Biomedical Knowledge Hub

Last commit Apr 13, 2021

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.

Last commit Jan 6, 2021

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

Last commit Mar 3, 2021

CheTo - Chemical Topic Modeling

Last commit Aug 10, 2017

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

Last commit Mar 4, 2024

A curated list of awesome computational cryo-EM methods.

Last commit Mar 21, 2021

decimer.ai

Last commit Jan 7, 2021

DECIMER 1.0: Deep Learning for Chemical Image Recognition using Transformers

Last commit Apr 29, 2021