Learning to Discover Medicines

• URL: http://arxiv.org/abs/2202.07096v1
• Date: Mon, 14 Feb 2022 23:43:51 GMT
• Title: Learning to Discover Medicines
• Authors: Tri Minh Nguyen, Thin Nguyen, Truyen Tran
• Abstract summary: Modern AI-enabled by powerful computing, large biomedical databases, and breakthroughs in deep learning-offers a new hope to break this loop. In this paper we review recent advances in AI methodologies that aim to crack this challenge. We organize the vast and rapidly growing literature of AI for drug discovery into three relatively stable sub-areas.
• Score: 21.744555824342264
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Discovering new medicines is the hallmark of human endeavor to live a better and longer life. Yet the pace of discovery has slowed down as we need to venture into more wildly unexplored biomedical space to find one that matches today's high standard. Modern AI-enabled by powerful computing, large biomedical databases, and breakthroughs in deep learning-offers a new hope to break this loop as AI is rapidly maturing, ready to make a huge impact in the area. In this paper we review recent advances in AI methodologies that aim to crack this challenge. We organize the vast and rapidly growing literature of AI for drug discovery into three relatively stable sub-areas: (a) representation learning over molecular sequences and geometric graphs; (b) data-driven reasoning where we predict molecular properties and their binding, optimize existing compounds, generate de novo molecules, and plan the synthesis of target molecules; and (c) knowledge-based reasoning where we discuss the construction and reasoning over biomedical knowledge graphs. We will also identify open challenges and chart possible research directions for the years to come.

Related papers

• Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey [75.47055414002571]
  The integration of biomolecular modeling with natural language (BL) has emerged as a promising interdisciplinary area at the intersection of artificial intelligence, chemistry and biology. We provide an analysis of recent advancements achieved through cross modeling of biomolecules and natural language.
  arXiv  Detail & Related papers  (2024-03-03T14:59:47Z)
• Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge [2.2814097119704058]
  Large language models (LLMs) are transforming the way information is retrieved with vast amounts of knowledge being summarized and presented. LLMs are prone to highlight the most frequently seen pieces of information from the training set and to neglect the rare ones. We introduce a novel information-retrieval method that leverages a knowledge graph to downsample these clusters and mitigate the information overload problem.
  arXiv  Detail & Related papers  (2024-02-19T18:31:11Z)
• Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs [54.223394825528665]
  We develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models. We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low.
  arXiv  Detail & Related papers  (2023-12-21T14:26:57Z)
• ProBio: A Protocol-guided Multimodal Dataset for Molecular Biology Lab [67.24684071577211]
  The challenge of replicating research results has posed a significant impediment to the field of molecular biology. We first curate a comprehensive multimodal dataset, named ProBio, as an initial step towards this objective. Next, we devise two challenging benchmarks, transparent solution tracking and multimodal action recognition, to emphasize the unique characteristics and difficulties associated with activity understanding in BioLab settings.
  arXiv  Detail & Related papers  (2023-11-01T14:44:01Z)
• Towards Unified AI Drug Discovery with Multiple Knowledge Modalities [5.232382666884214]
  We propose KEDD, a unified, end-to-end, and multimodal deep learning framework. It optimally incorporates both structured and unstructured knowledge for vast AI drug discovery tasks. Our framework achieves a deeper understanding of molecule entities, brings significant improvements over state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-04-17T13:15:16Z)
• Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey [6.288056740658763]
  Graph Machine Learning (GML) has gained considerable attention for its exceptional ability to model graph-structured biomedical data. Recent studies have proposed integrating external biomedical knowledge into the GML pipeline to realise more precise and interpretable drug discovery.
  arXiv  Detail & Related papers  (2023-02-16T12:38:01Z)
• ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery [79.08833067391093]
  Real-world pharmaceutical datasets often exhibit highly imbalanced distribution. We introduce ImDrug, a benchmark with an open-source Python library which consists of 4 imbalance settings, 11 AI-ready datasets, 54 learning tasks and 16 baseline algorithms tailored for imbalanced learning. It provides an accessible and customizable testbed for problems and solutions spanning a broad spectrum of the drug discovery pipeline.
  arXiv  Detail & Related papers  (2022-09-16T13:35:57Z)
• A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language [63.60376252491507]
  We propose a molecular multimodal foundation model which is pretrained from molecular graphs and their semantically related textual data. We believe that our model would have a broad impact on AI-empowered fields across disciplines such as biology, chemistry, materials, environment, and medicine.
  arXiv  Detail & Related papers  (2022-09-12T00:56:57Z)
• Discovering Drug-Target Interaction Knowledge from Biomedical Literature [107.98712673387031]
  The Interaction between Drugs and Targets (DTI) in human body plays a crucial role in biomedical science and applications. As millions of papers come out every year in the biomedical domain, automatically discovering DTI knowledge from literature becomes an urgent demand in the industry. We explore the first end-to-end solution for this task by using generative approaches. We regard the DTI triplets as a sequence and use a Transformer-based model to directly generate them without using the detailed annotations of entities and relations.
  arXiv  Detail & Related papers  (2021-09-27T17:00:14Z)
• Artificial Intelligence in Drug Discovery: Applications and Techniques [33.59138543942538]
  Various AI techniques have been used in a wide range of applications, such as virtual screening and drug design. We first give an overview on drug discovery and discuss related applications, which can be reduced to two major tasks. We then discuss common data resources, molecule representations and benchmark platforms.
  arXiv  Detail & Related papers  (2021-06-09T20:46:44Z)
• Generative chemistry: drug discovery with deep learning generative models [0.0]
  This paper reviews the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused.
  arXiv  Detail & Related papers  (2020-08-20T14:38:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.