An automated scoring function to facilitate and standardize the evaluation of goal-directed generative models for de novo molecular design

De Novo Drug Design with RNNs and Transformers

The official codebase of the paper "Chemical language modeling with structured state space sequence models"

Systemic Evolutionary Chemical Space Exploration for Drug Discovery

Multi-target de novo molecular generator conditioned on AlphaFold's latent protein embeddings.

Python-based GUI to collect Feedback of Chemist in Molecules

Research repo for AI aided drug discovery, de novo drug development and related topics

Code pipeline for the PaccMann^RL in iScience: https://www.cell.com/iscience/fulltext/S2589-0042(21)00237-6

HELM-GPT: de novo macrocyclic peptide design using generative pre-trained transformer

Open source code for DyScore

Code for paper on automation of discovery and synthesis of targeted molecules: https://iopscience.iop.org/article/10.1088/2632-2153/abe808

TAGMol: Target-Aware Gradient-guided Molecule Generation (ICML'24 ML4LMS Workshop)

A recurrent neural network (RNN) that generates drug-like molecules for drug discovery.

Inverse Reinforcement Learning-based Structural Evolution of Small Molecules

De novo Drug Design via Binary Representations of SMILES for avoiding the Posterior Collapse Problem (BIBM 2021)

The official codebase of the paper "The Jungle of Generative Drug Discovery: Traps, Treasures, and Ways Out""

De novo drug discovery of protein-specific using Transformer Neural Network

DL Pipeline for Antibiotic Discovery

de novo generation of synthetically feasible structures matching a 3D pharmacopohre model

This project is an advanced artificial intelligence and machine learning platform for de novo protein design and data analysis. Using generative neural networks, it empowers researchers to generate protein sequences, predict 3D structures, and extract insights from biological data for bioengineering breakthroughs.