4.6 Article

Pushing the Boundaries of Molecular Property Prediction for Drug Discovery with Multitask Learning BERT Enhanced by SMILES Enumeration

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Review Chemistry, Multidisciplinary

Applications of Deep Learning in Molecule Generation and Molecular Property Prediction

W. Patrick Walters et al.

Summary: Recent advances in computer hardware and software have led to a revolution in deep neural networks that has impacted various fields including drug discovery. Deep learning methods have significantly improved the prediction of molecular properties and the generation of new molecules in drug discovery programs. The application of deep learning has led to state-of-the-art performance in quantitative structure-activity relationships (QSARs), but challenges remain in data quantity and quality for predictive model design. Researchers are also exploring methods for assessing the confidence in models and generating new molecules based on existing data using deep learning techniques.

ACCOUNTS OF CHEMICAL RESEARCH (2021)

Article Engineering, Electrical & Electronic

A Comprehensive Survey on Transfer Learning

Fuzhen Zhuang et al.

Summary: Transfer learning aims to improve the performance of target learners by transferring knowledge from related source domains, reducing the reliance on target-domain data. This survey aims to systematize and summarize existing research studies in order to help readers understand the current status and ideas in the area of transfer learning.

PROCEEDINGS OF THE IEEE (2021)

Article Biochemical Research Methods

MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction

Xiao-Chen Zhang et al.

Summary: This study introduces a molecular graph BERT (MG-BERT) model that integrates graph neural network mechanisms and utilizes a self-supervised learning strategy for pretraining, enhancing the model's contextual sensitivity and achieving outstanding performance in molecular property prediction.

BRIEFINGS IN BIOINFORMATICS (2021)

Article Biochemical Research Methods

Learning to SMILES: BAN-based strategies to improve latent representation learning from molecules

Cheng-Kun Wu et al.

Summary: Computational methods are essential for accelerating drug discovery. Deep learning provides a data-driven approach to learn complex data representations, but current models often lack labeled data, hindering their generalization ability. The study introduces a novel attention mechanism to extract key features from SMILES strings and utilizes SMILES enumeration for data augmentation and bias correction, improving the performance of features learned from SMILES strings.

BRIEFINGS IN BIOINFORMATICS (2021)

Review Biotechnology & Applied Microbiology

Review of unsupervised pretraining strategies for molecules representation

Linhui Yu et al.

Summary: In recent years, computer-assisted techniques have made significant progress in drug discovery, but the limited labeled data problem remains a challenge. Unsupervised pretraining is an effective strategy to address this issue, improving model performance in specific tasks.

BRIEFINGS IN FUNCTIONAL GENOMICS (2021)

Article Multidisciplinary Sciences

Highly accurate protein structure prediction with AlphaFold

John Jumper et al.

Summary: Proteins are essential for life, and accurate prediction of their structures is a crucial research problem. Current experimental methods are time-consuming, highlighting the need for accurate computational approaches to address the gap in structural coverage. Despite recent progress, existing methods fall short of atomic accuracy in protein structure prediction.

NATURE (2021)

Article Computer Science, Artificial Intelligence

Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning

Jike Wang et al.

Summary: The MCMG approach combines a conditional transformer and reinforcement learning algorithms through knowledge distillation to generate molecules that satisfy multiple constraints, providing an efficient way to traverse large and complex chemical space in search of novel compounds.

NATURE MACHINE INTELLIGENCE (2021)

Article Chemistry, Medicinal

Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism

Zhaoping Xiong et al.

JOURNAL OF MEDICINAL CHEMISTRY (2020)

Article Chemistry, Multidisciplinary

Transformer-CNN: Swiss knife for QSAR modeling and interpretation

Pavel Karpov et al.

JOURNAL OF CHEMINFORMATICS (2020)

Review Chemistry, Multidisciplinary

Molecular representations in AI-driven drug discovery: a review and practical guide

Laurianne David et al.

JOURNAL OF CHEMINFORMATICS (2020)

Article Computer Science, Information Systems

A Deep Learning-Based Chemical System for QSAR Prediction

ShanShan Hu et al.

IEEE Journal of Biomedical and Health Informatics (2020)

Review Biotechnology & Applied Microbiology

Applications of machine learning in drug discovery and development

Jessica Vamathevan et al.

NATURE REVIEWS DRUG DISCOVERY (2019)

Review Computer Science, Artificial Intelligence

A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures

Yong Yu et al.

NEURAL COMPUTATION (2019)

Review Chemistry, Medicinal

A Survey of Multi-task Learning Methods in Chemoinformatics

Sergey Sosnin et al.

MOLECULAR INFORMATICS (2019)

Article Biochemical Research Methods

DeepDTA: deep drug-target binding affinity prediction

Hakime Ozturk et al.

BIOINFORMATICS (2018)

Review Pharmacology & Pharmacy

Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks

Fahimeh Ghasemi et al.

DRUG DISCOVERY TODAY (2018)

Article Chemistry, Multidisciplinary

MoleculeNet: a benchmark for molecular machine learning

Zhenqin Wu et al.

CHEMICAL SCIENCE (2018)

Review Pharmacology & Pharmacy

Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges

Rodolfo S. Simoes et al.

FRONTIERS IN PHARMACOLOGY (2018)

Review Public, Environmental & Occupational Health

Mutagenic and carcinogenic structural alerts and their mechanisms of action

Alja Plosnik et al.

ARHIV ZA HIGIJENU RADA I TOKSIKOLOGIJU-ARCHIVES OF INDUSTRIAL HYGIENE AND TOXICOLOGY (2016)

Article Chemistry, Medicinal

Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships

Robert P. Sheridan et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2016)

Review Biochemistry & Molecular Biology

Machine Learning Techniques and Drug Design

J. C. Gertrudes et al.

CURRENT MEDICINAL CHEMISTRY (2012)

Review Pharmacology & Pharmacy

Drug discovery in pharmaceutical industry: productivity challenges and trends

Ish Khanna

DRUG DISCOVERY TODAY (2012)

Article Biochemistry & Molecular Biology

ChEMBL: a large-scale bioactivity database for drug discovery

Anna Gaulton et al.

NUCLEIC ACIDS RESEARCH (2012)

Article Chemistry, Medicinal

Extended-Connectivity Fingerprints

David Rogers et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2010)

Review Biochemical Research Methods

Recent advances in computer-aided drug design

Chun Meng Song et al.

BRIEFINGS IN BIOINFORMATICS (2009)