/tools

PMDM is a software tool that enables the generation of 3D bioactive molecules and lead optimization by utilizing a dual diffusion model. It supports molecular docking and provides benchmarks for evaluating generated molecules, making it a valuable resource for drug discovery and molecular design.

MolCRAFT is a series of projects aimed at developing deep learning models for structure-based drug design and molecule optimization. It introduces novel methodologies for generating molecules with high binding affinity and stable 3D conformations, addressing critical challenges in the field.

Summit is a set of tools designed for optimizing chemical processes, particularly reactions, using machine learning techniques. It includes various optimization strategies and benchmarks to enhance the efficiency of reaction optimization in the fine chemicals industry.

DrugAssist is a large language model aimed at optimizing molecules, making it a valuable tool in drug discovery. It includes a dataset for training and facilitates the generation and optimization of molecular structures.

Sella is a Python software package designed for saddle point optimization and minimization of atomic systems. It facilitates the identification of first order saddle points, which is crucial in molecular simulations and understanding reaction pathways.

Dirichlet Flow Matching is a tool designed for DNA sequence generation and optimization, particularly in the context of enhancer and promoter design. It utilizes advanced machine learning techniques to facilitate molecular design tasks.

Pysisyphus is a Python suite designed for exploring potential energy surfaces in both ground and excited states. It provides methods for searching stationary points and calculating minimum energy paths, making it a valuable tool for molecular simulations and quantum chemistry workflows.

MolGym is a tool that utilizes reinforcement learning to design molecules in three-dimensional space, guided by principles of quantum mechanics. It allows users to train agents to build molecular structures by placing atoms on a canvas, facilitating the generation and optimization of new molecular designs.

g-xTB is a development version of a general-purpose semiempirical quantum mechanical method that approximates molecular properties. It supports geometry optimization and numerical gradient calculations, making it useful for various molecular simulations and analyses.

dyMEAN is a tool for end-to-end full-atom antibody design, enabling the generation and optimization of antibody structures based on specific epitope definitions. It includes functionalities for complex structure prediction and affinity optimization, making it a valuable resource in drug discovery and protein design.

CatLearn is a machine learning environment focused on atomic-scale modeling for surface science and catalysis. It provides utilities for building and testing machine learning models, including Gaussian Processes for predicting molecular properties and optimizing atomic structures.

DrugHIVE is a software tool that implements a deep hierarchical variational autoencoder for structure-based drug design. It allows for the generation and optimization of ligands, making it a valuable resource in the field of molecular design and drug discovery.

Psikit is a wrapper library for Psi4 and RDKit that facilitates quantum chemistry calculations, including the prediction of molecular properties such as HOMO and LUMO energies, structure optimization, and charge calculations. It is designed to assist in molecular design and cheminformatics tasks.

MEAN is a tool for conditional antibody design utilizing a multi-channel equivariant attention network. It provides functionalities for redesigning antibody CDRs and optimizing binding affinities, making it a valuable resource in the field of molecular design and drug discovery.

BAGEL is a customizable Python framework for programmable protein design that formalizes the design task as an optimization over an energy landscape. It includes components for defining energy terms, oracles for model integration, and algorithms for sequence sampling and optimization.

ProteinDT is a framework for text-guided protein design that allows for the generation and editing of protein sequences. It utilizes advanced machine learning techniques to optimize protein properties and facilitate design tasks.

ProteinDJ is a protein design pipeline that integrates multiple software packages to facilitate the design and optimization of protein structures. It allows users to generate new protein sequences and structures through various design modes, leveraging advanced modeling techniques like RFdiffusion and AlphaFold2.

MolDQN-pytorch is a PyTorch implementation of a deep reinforcement learning approach for optimizing molecular properties. It allows users to train models for property optimization tasks, making it a valuable tool in the field of molecular design and drug discovery.

Architector is a Python package that generates 3D chemical structures of organometallic complexes from minimal 2D information. It supports high-throughput in-silico construction and is capable of producing chemically sensible conformers and stereochemistry for various metal complexes.

The NVIDIA BioNeMo blueprint provides a framework for generative virtual screening in drug discovery, utilizing advanced AI models to design and optimize small molecules and predict protein-ligand interactions. It integrates various tools for protein structure prediction and molecular generation, facilitating efficient drug discovery workflows.

The riff_diff_protflow repository provides an implementation of the RiffDiff pipeline, which is designed for generating and optimizing enzyme structures from theozymes. It utilizes various protein design tools and scripts to create fragment libraries and refine structures, facilitating the design of novel proteins.

This repository provides an implementation of Thompson Sampling for virtual screening of un-enumerated libraries in molecular design. It allows users to efficiently search and score potential molecules based on various scoring functions, facilitating the exploration of chemical space.

The ppqm package facilitates the integration of RDKit with various quantum chemistry software, allowing users to perform molecular property calculations and optimizations. It serves as a bridge for cheminformatics applications, enabling efficient quantum chemistry computations in Python.

This tool implements a reinforced genetic algorithm for structure-based drug design, utilizing neural models to enhance the efficiency of molecular optimization. It aims to intelligently explore chemical space to identify potential drug candidates with improved binding affinity.