ribesstefano
Computer engineer passionate about AI and deep learning, chemistry, and FPGAs 🚀
@ailab-bio
ribesstefano's Stars
lllyasviel/Fooocus
Focus on prompting and generating
google-research/tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.
meta-llama/llama3
The official Meta Llama 3 GitHub site
state-spaces/mamba
Mamba SSM architecture
Z3Prover/z3
The Z3 Theorem Prover
huggingface/accelerate
🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
ddangelov/Top2Vec
Top2Vec learns jointly embedded topic, document and word vectors.
lamini-ai/lamini
chemprop/chemprop
Message Passing Neural Networks for Molecule Property Prediction
quantumiracle/Popular-RL-Algorithms
PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..
PatWalters/practical_cheminformatics_tutorials
Practical Cheminformatics Tutorials
generatebio/chroma
A generative model for programmable protein design
biotite-dev/biotite
A comprehensive library for computational molecular biology
napolux/paroleitaliane
Liste di parole italiane
GT4SD/gt4sd-core
GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process.
BayraktarLab/cell2location
Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics (cell2location model)
lamini-ai/llm-classifier
Classify data instantly using an LLM
open-reaction-database/ord-data
Official data repository for the Open Reaction Database
microsoft/protein-frame-flow
Fast protein backbone generation with SE(3) flow matching.
a-r-j/ProteinWorkshop
Benchmarking framework for protein representation learning. Includes a large number of pre-training and downstream task datasets, models and training/task utilities. (ICLR 2024)
theislab/cpa
The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.
MolecularAI/PaRoutes
Home of the PaRoutes framework for benchmarking multi-step retrosynthesis predictions.
OpenBioML/protein-lm-scaling
molML/s4-for-de-novo-drug-design
The official codebase of the paper "Chemical language modeling with structured state space sequence models"
RekerLab/DeepDelta
DeepDelta is a pairwise deep learning approach based on Chemprop that processes two molecules simultaneously and learns to predict property differences between two molecules.
coleygroup/polymer-chemprop
Message Passing Neural Networks for Molecule Property Prediction
Dunni3/keypoint-diffusion
A diffusion model for structure-based drug design with faster inference from learned representations of protein structure.
biomed-AI/DRlinker
ribesstefano/Mapping-Multiple-LSTM-Models-on-FPGAs
Includes the SVD-based approximation algorithms for compressing deep learning models and the FPGA accelerators exploiting such approximation mechanism, as described in the paper Mapping multiple LSTM models on FPGAs.
ailab-bio/GraphINVENT-lite
Core functionalities of GraphINVENT in a smaller, more user-friendly package.