Deep Reinforcement Learning for de-novo Drug Design
- deeplearning
- reinforcement-learning
- cheminformatics
- molecular-modeling
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Deep Reinforcement Learning for de-novo Drug Design
Fusing Histology and Genomics via Deep Learning - IEEE TMI
TranslatedLLM teaching material for high school students: based on Karpathy's microgpt, for Google Colab
MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction (Bioinformatics)
Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning - Cancer Cell
Usiigaci: stain-free cell tracking in phase contrast microscopy enabled by supervised machine learning
An enhanced interactive Shell for Common Lisp (based on the Jupyter protocol)
This repository is a collection of TTS Models in TFLite
Wrapper for RDKit's RunReactants to improve stereochemistry handling
scPerturb: A resource and a python/R tool for single-cell perturbation data
🏃 The go-to single-cell Foundation Model
Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.
Tools for pen plotting in Python
⬛ Python Individual Conditional Expectation Plot Toolbox
A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design
Integrated pipeline for multiplexed image analysis
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets --- https://arxiv.org/abs/2004.07999
tutorials made for biologists to learn deep learning
Cyber Defence Monitoring Course Suite :: Suricata, Arkime (and others in the past)
a nose plugin for finding and running IPython notebooks as nose tests
Code repository for The Automation Ahead series, showcasing practical examples for GenAI-driven automation in investments. Each installment dives into specific tools, techniques, and use cases to help professionals assess and apply GenAI effectively.
Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders
Machine learning for Analysis of Proteomics in Spatial biology - Nature Communications
High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations