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Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition
یادگیری ماشین و رویکردهای مبتنی بر هوش مصنوعی برای کشف لیگاند زیست فعال و تشخیص GPCR-لیگاند-2020 In the last decade, machine learning and artificial intelligence applications have received a significant boost in
performance and attention in both academic research and industry. The success behind most of the recent stateof-
the-art methods can be attributed to the latest developments in deep learning. When applied to various
scientific domains that are concerned with the processing of non-tabular data, for example, image or text, deep
learning has been shown to outperform not only conventional machine learning but also highly specialized tools
developed by domain experts. This review aims to summarize AI-based research for GPCR bioactive ligand
discovery with a particular focus on the most recent achievements and research trends. To make this article
accessible to a broad audience of computational scientists, we provide instructive explanations of the underlying
methodology, including overviews of the most commonly used deep learning architectures and feature representations
of molecular data. We highlight the latest AI-based research that has led to the successful discovery
of GPCR bioactive ligands. However, an equal focus of this review is on the discussion of machine learning-based
technology that has been applied to ligand discovery in general and has the potential to pave the way for
successful GPCR bioactive ligand discovery in the future. This review concludes with a brief outlook highlighting
the recent research trends in deep learning, such as active learning and semi-supervised learning, which have
great potential for advancing bioactive ligand discovery. Keywords: Molecular representations | GPCR ligands | Drug discovery | Deep learning | Machine learning | Graph convolutional neural networks |
مقاله انگلیسی |
2 |
Accelerating materials science with high-throughput computations and machine learning
تسریع در علم مواد با محاسبات با توان بالا و یادگیری ماشین-2019 With unprecedented amounts of materials data generated from experiments as well as high-throughput density
functional theory calculations, machine learning techniques has the potential to greatly accelerate materials
discovery and design. Here, we review our efforts in the Materials Virtual Lab to integrate software automation,
data generation and curation and machine learning to (i) design and optimize technological materials for energy
storage, energy efficiency and high-temperature alloys; (ii) develop scalable quantum-accurate models, and (iii)
enhance the speed and accuracy in interpreting characterization spectra. Keywords: Machine learning | High-throughput | Materials discovery | Materials design | Multi-scale models |
مقاله انگلیسی |
3 |
The opportunities of mining historical and collective data in drug discovery
فرصت های کاوش داده های تاریخی و جمعی در کشف مواد مخدر-2015 Vast amounts of bioactivity data have been generated for small molecules
across public and corporate domains. Biological signatures, either derived
from systematic profiling efforts or from existing historical assay data,
have been successfully employed for small molecule mechanism-of-action
elucidation, drug repositioning, hit expansion and screening subset
design. This article reviews different types of biological descriptors and
applications, and we demonstrate how biological data can outlive the
original purpose or project for which it was generated. By comparing 150
HTS campaigns run at Novartis over the past decade on the basis of their
active and inactive chemical matter, we highlight the opportunities and
challenges associated with cross-project learning in drug discovery. |
مقاله انگلیسی |