Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
- diffusion-models
- docking
- binding
- computational-biology
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Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
PettingZoo and Gymnasium bindings for popular reinforcement learning environments outside of Farama
OCaml bindings for PyTorch
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OCaml bindings for TensorFlow
Ruby language bindings for LIBSVM
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Go bindings for FANN, library for artificial neural networks
We would like to maintain a list of resources which aim to solve molecular docking and other closely related tasks.
TensorFlow.Delphi (TF.Delphi) provides a Delphi(Pascal) Standard binding for TensorFlow. It aims to implement the complete Tensorflow API in Delphi which allows Pascal developers to develop, train and deploy Machine Learning models with the Pascal Delphi
FANN (Fast Artifical Neural Network) binding in Crystal
Ultra high-performance secp256k1 ECC engine | Python, Node.js, Rust, Go, C#, Swift, Java bindings | CUDA, Metal, OpenCL GPU | ECDSA, Schnorr, FROST, MuSig2, BIP-352 | 15+ platforms
Catkin-friendly C++ bindings for tensorflow.
Keras4Delphi is a high-level neural networks API, written in Pascal with Python Binding
Python bindings for the Coq interactive proof assistant
MXNet (AI/ML) bindings for the Crystal language.
Micropython binding for the ESP32 DL AI vision models like face detection / recognition, imagenet classifier or pedestrian (human) detection
An Icon classifier made with TFLite Model Maker and deployed to Android with ML Model Binding.