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Diversity oriented Deep Reinforcement Learning for targeted molecule generation

Journal of cheminformatics, 2021-03, Vol.13 (1), p.21-21, Article 21

2021

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Enhancing schizophrenia phenotype prediction from genotype data through knowledge-driven deep neural network models

Genomics (San Diego, Calif.), 2024-09, Vol.116 (5), p.110910, Article 110910

2024

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Few-shot learning via graph embeddings with convolutional networks for low-data molecular property prediction

Neural computing & applications, 2023-06, Vol.35 (18), p.13167-13185

2023

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Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction

PLoS computational biology, 2016-11, Vol.12 (11), p.e1005219-e1005219

2016

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Drug-Target Interaction Prediction: End-to-End Deep Learning Approach

IEEE/ACM transactions on computational biology and bioinformatics, 2021-11, Vol.18 (6), p.2364-2374

2021

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Enhancing reinforcement learning for de novo molecular design applying self-attention mechanisms

Briefings in bioinformatics, 2023-09, Vol.24 (6)

2023

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Predicting drug activity against cancer through genomic profiles and SMILES

Artificial intelligence in medicine, 2024-04, Vol.150, p.102820, Article 102820

2024

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A Hybrid GNN Approach for Improved Molecular Property Prediction

Journal of computational biology, 2024-07

2024

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DTITR: End-to-end drug–target binding affinity prediction with transformers

Computers in biology and medicine, 2022-08, Vol.147, p.105772-105772, Article 105772

2022

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CroP—Coordinated Panel visualization for biological networks analysis

Bioinformatics, 2020-02, Vol.36 (4), p.1298-1299

2020

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Detecting outliers in case-control cohorts for improving deep learning networks on Schizophrenia prediction

Journal of integrative bioinformatics, 2024-08, Vol.21 (2), p.67-76

2024

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Explainable deep drug–target representations for binding affinity prediction

BMC bioinformatics, 2022-06, Vol.23 (1), p.1-237, Article 237

2022

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Combining graph neural networks and transformers for few-shot nuclear receptor binding activity prediction

Journal of cheminformatics, 2024-09, Vol.16 (1), p.109-19, Article 109

2024

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The Road to Personalized Medicine in Alzheimer's Disease: The Use of Artificial Intelligence

Biomedicines, 2022-01, Vol.10 (2), p.315

2022

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Interactive and coordinated visualization approaches for biological data analysis

Briefings in bioinformatics, 2019-07, Vol.20 (4), p.1513-1523

2019

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Multivariate Data Exploration Through Coordinated Views

IEEE access, 2022, Vol.10, p.78788-78817

2022

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Designing optimized drug candidates with Generative Adversarial Network

Journal of cheminformatics, 2022-06, Vol.14 (1), p.40-40, Article 40

2022

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Artificial intelligence for prediction of biological activities and generation of molecular hits using stereochemical information

Journal of computer-aided molecular design, 2023-12, Vol.37 (12), p.791-806

2023

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RecRWR: A Recursive Random Walk Method for Improved Identification of Diseases

BioMed research international, 2015-01, Vol.2015 (2015), p.1-7

2015

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Deep generative model for therapeutic targets using transcriptomic disease-associated data—USP7 case study

Briefings in bioinformatics, 2022-07, Vol.23 (4)

2022

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Concept-based query expansion for retrieving gene related publications from MEDLINE

BMC bioinformatics, 2010-04, Vol.11 (1), p.212-212, Article 212

2010

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On the Quantization of Recurrent Neural Networks for Smiles Generation

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023, p.1-5

2023

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FSM-DDTR: End-to-end feedback strategy for multi-objective De Novo drug design using transformers

Computers in biology and medicine, 2023-09, Vol.164, p.107285-107285, Article 107285

2023

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Fighting Fire with Fire: Computational Prediction of Microbial Targets for Bacteriocins

Bioinformatics and Biomedical Engineering, p.221-234

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Exploring Time-Series Through Force-Directed Timelines

2020 24th International Conference Information Visualisation (IV), 2020, p.328-335

2020

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Diversity oriented Deep Reinforcement Learning for targeted molecule generation

Enhancing schizophrenia phenotype prediction from genotype data through knowledge-driven deep neural network models

Few-shot learning via graph embeddings with convolutional networks for low-data molecular property prediction

Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction

Drug-Target Interaction Prediction: End-to-End Deep Learning Approach

Enhancing reinforcement learning for de novo molecular design applying self-attention mechanisms

Predicting drug activity against cancer through genomic profiles and SMILES

A Hybrid GNN Approach for Improved Molecular Property Prediction

DTITR: End-to-end drug–target binding affinity prediction with transformers

CroP—Coordinated Panel visualization for biological networks analysis

Detecting outliers in case-control cohorts for improving deep learning networks on Schizophrenia prediction

Explainable deep drug–target representations for binding affinity prediction

Combining graph neural networks and transformers for few-shot nuclear receptor binding activity prediction

The Road to Personalized Medicine in Alzheimer's Disease: The Use of Artificial Intelligence

Interactive and coordinated visualization approaches for biological data analysis

Multivariate Data Exploration Through Coordinated Views

Designing optimized drug candidates with Generative Adversarial Network

Artificial intelligence for prediction of biological activities and generation of molecular hits using stereochemical information

RecRWR: A Recursive Random Walk Method for Improved Identification of Diseases

Deep generative model for therapeutic targets using transcriptomic disease-associated data—USP7 case study

Concept-based query expansion for retrieving gene related publications from MEDLINE

On the Quantization of Recurrent Neural Networks for Smiles Generation

FSM-DDTR: End-to-end feedback strategy for multi-objective De Novo drug design using transformers

Fighting Fire with Fire: Computational Prediction of Microbial Targets for Bacteriocins

Exploring Time-Series Through Force-Directed Timelines

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