12 skills found
liugangcode / Torch Moleculetorch-molecule is a deep learning package for molecular discovery, designed with an sklearn-style interface for property prediction, inverse design and representation learning.
bowen-gao / DrugCLIP[NeurIPS 2023] DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening
hwwang55 / MolRChemical-Reaction-Aware Molecule Representation Learning
yangnianzu0515 / MoleRecThe official implementation of our paper "MoleRec: Combinatorial Drug Recommendation with Substructure-Aware Molecular Representation Learning" (TheWebConf 2023).
yangnianzu0515 / MoleOODOfficial implementation for the paper "Learning Substructure Invariance for Out-of-Distribution Molecular Representations" (NeurIPS 2022).
jbr-ai-labs / Lipophilicity PredictionCode for "Lipophilicity Prediction with Multitask Learning and Molecular Substructures Representation" paper. Machine Learning for Molecules Workshop @ NeurIPS 2020
SongtaoLiu0823 / FusionRetro[ICML 2023] FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
yikunpku / AtomasICLR 2025: We propose Atomas, a hierarchical molecular representation learning framework that jointly learns representations from SMILES strings and text. We design a Hierarchical Adaptive Alignment model to automatically learn the fine-grained fragment correspondence between two modalities and align these representations at three semantic levels.
545487677 / OCNet[npj Computational Materials] This is the official code for the paper “Virtual Characterization via Knowledge-Enhanced Representation Learning: from Organic Conjugated Molecules to Devices”
yifeiwang15 / MotifConvThis is the official implementation of "Motif-based Graph Representation Learning with Application to Chemical Molecules".
ASethi04 / Generation Of Novel Drug Molecules With Specific Protein Targets Through A Graph Network And Custom ABSTRACT Creation of novel drug molecules is a time consuming and expensive process. Current methods require manually synthesizing thousands of molecules to develop a single viable lead candidate. In silico prediction of drug–target interactions (DTI) is necessary for the development of new drugs. In this research, I developed a novel artificial intelligence model capable of predicting DTIs based on drug chemical structure data. Using 445 drugs in the DrugBank database, I created a graph network to represent the compound chemical structures and predicted DTIs based on structural similarity. Inferring over the graph with a modified nearest neighbors to predict a new drug’s protein interactions achieved an area under the receiver operating characteristic of 0.93. Furthermore, I developed a generative machine learning model to create novel drug molecules with specific DTI profiles. Using the same DrugBank data, I created a custom Conditional Variational Autoencoder (CVAE) to encode the string representation of drug compound structures and their associated DTIs. The DTI profiles are incorporated as conditions in the encoder and decoder of the model, allowing generation of novel drug molecules with specified DTIs. As proof of concept, I show that the CVAE can generate similar (but not identical) molecules that are still chemically valid. Novel drugs generated by the CVAE achieved an average similarity compound score of 0.70 relative to their corresponding molecules in the test set. This study advances the possibility of low-cost and efficient drug development by proposing an in-silico method for targeted lead candidate molecule generation.
xduan7 / MoReLMolecule Representation Learning Toolkit
