A Comprehensive Guide to Enhancing Antibiotic Discovery Using Machine Learning Derived Bio-computation

• URL: http://arxiv.org/abs/2411.06009v1
• Date: Fri, 08 Nov 2024 23:04:42 GMT
• Title: A Comprehensive Guide to Enhancing Antibiotic Discovery Using Machine Learning Derived Bio-computation
• Authors: Khartik Uppalapati, Eeshan Dandamudi, S. Nick Ice, Gaurav Chandra, Kirsten Bischof, Christian L. Lorson, Kamal Singh,
• Abstract summary: We provide an overview of different AI and ML tools that can be used to streamline and accelerate the drug discovery process. We address limitations in AI-based drug discovery and development, including the scarcity of high-quality data. We discuss how AI and ML can expedite the discovery of new antibiotics to combat the problem of worldwide antimicrobial resistance.
• Score: 0.7739316058960921
• License:
• Abstract: Traditional drug discovery is a long, expensive, and complex process. Advances in Artificial Intelligence (AI) and Machine Learning (ML) are beginning to change this narrative. Here, we provide a comprehensive overview of different AI and ML tools that can be used to streamline and accelerate the drug discovery process. By using data sets to train ML algorithms, it is possible to discover drugs or drug-like compounds relatively quickly, and efficiently. Additionally, we address limitations in AI-based drug discovery and development, including the scarcity of high-quality data to train AI models and ethical considerations. The growing impact of AI on the pharmaceutical industry is also highlighted. Finally, we discuss how AI and ML can expedite the discovery of new antibiotics to combat the problem of worldwide antimicrobial resistance (AMR).

Related papers

• DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration [31.892593155710625]
  We introduce DrugAgent, a multi-agent framework aimed at automating machine learning (ML) programming in drug discovery. DrugAgent incorporates domain expertise by identifying specific requirements and building domain-specific tools, while systematically exploring different ideas to find effective solutions. For example, DrugAgent is able to complete the ML programming pipeline end-to-end, from data acquisition to performance evaluation for the ADMET prediction task, and finally select the best model.
  arXiv  Detail & Related papers  (2024-11-24T03:06:59Z)
• Physical formula enhanced multi-task learning for pharmacokinetics prediction [54.13787789006417]
  A major challenge for AI-driven drug discovery is the scarcity of high-quality data. We develop a formula enhanced mul-ti-task learning (PEMAL) method that predicts four key parameters of pharmacokinetics simultaneously. Our experiments reveal that PEMAL significantly lowers the data demand, compared to typical Graph Neural Networks.
  arXiv  Detail & Related papers  (2024-04-16T07:42:55Z)
• Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey [11.331107195122147]
  The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. As these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. XAI is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models.
  arXiv  Detail & Related papers  (2023-09-21T15:36:06Z)
• Artificial Intelligence for Drug Discovery: Are We There Yet? [0.08306867559432653]
  Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small molecule drugs.
  arXiv  Detail & Related papers  (2023-07-13T01:51:26Z)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)
• 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)
• Learning to Discover Medicines [21.744555824342264]
  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.
  arXiv  Detail & Related papers  (2022-02-14T23:43:51Z)
• DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations [90.27736364704108]
  We present DrugOOD, a systematic OOD dataset curator and benchmark for AI-aided drug discovery. DrugOOD comes with an open-source Python package that fully automates benchmarking processes. We focus on one of the most crucial problems in AIDD: drug target binding affinity prediction.
  arXiv  Detail & Related papers  (2022-01-24T12:32:48Z)
• A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction [70.11080854486953]
  We propose a user-centred framework for XAI that focuses on its social-interactive aspect. The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
  arXiv  Detail & Related papers  (2021-09-27T09:56:23Z)
• 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)
• Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
  We show how AI can empower the IoT to make it faster, smarter, greener, and safer. First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
  arXiv  Detail & Related papers  (2020-11-17T13:14:28Z)

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.