Artificial Intelligence for Drug Discovery: Are We There Yet?
- URL: http://arxiv.org/abs/2307.06521v1
- Date: Thu, 13 Jul 2023 01:51:26 GMT
- Title: Artificial Intelligence for Drug Discovery: Are We There Yet?
- Authors: Catrin Hasselgren and Tudor I. Oprea
- Abstract summary: 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.
- Score: 0.08306867559432653
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Drug discovery is adapting to novel technologies such as data science,
informatics, and artificial intelligence (AI) to accelerate effective treatment
development while reducing costs and animal experiments. AI is transforming
drug discovery, as indicated by increasing interest from investors, industrial
and academic scientists, and legislators. Successful drug discovery requires
optimizing properties related to pharmacodynamics, pharmacokinetics, and
clinical outcomes. 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. AI technologies, such as generative chemistry, machine
learning, and multi-property optimization, have enabled several compounds to
enter clinical trials. The scientific community must carefully vet known
information to address the reproducibility crisis. The full potential of AI in
drug discovery can only be realized with sufficient ground truth and
appropriate human intervention at later pipeline stages.
Related papers
- Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials [49.19897427783105]
The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift.
We investigate how these advanced computational models can uncover target-disease linkage, interpret complex biomedical data, enhance drug molecule design, predict drug efficacy and safety profiles, and facilitate clinical trial processes.
arXiv Detail & Related papers (2024-09-06T02:03:38Z) - DrugAgent: Explainable Drug Repurposing Agent with Large Language Model-based Reasoning [10.528489471229946]
We propose a multi-agent framework to enhance the drug repurposing process using state-of-the-art machine learning techniques and knowledge integration.
Our framework comprises several specialized agents: an AI Agent trains robust drug-target interaction (DTI) models; a Knowledge Graph Agent utilizes the drug-gene interaction database (DGIdb) to systematically extract DTIs.
By integrating outputs from these agents, our system effectively harnesses diverse data sources, including external databases, to propose viable repurposing candidates.
arXiv Detail & Related papers (2024-08-23T21:24:59Z) - 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) - Emerging Opportunities of Using Large Language Models for Translation
Between Drug Molecules and Indications [6.832024637226738]
We propose a new task, which is the translation between drug molecules and corresponding indications.
The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases.
arXiv Detail & Related papers (2024-02-14T21:33:13Z) - Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural
Network with Biomedical Network [69.16939798838159]
We propose EmerGNN, a graph neural network (GNN) that can effectively predict interactions for emerging drugs.
EmerGNN learns pairwise representations of drugs by extracting the paths between drug pairs, propagating information from one drug to the other, and incorporating the relevant biomedical concepts on the paths.
Overall, EmerGNN has higher accuracy than existing approaches in predicting interactions for emerging drugs and can identify the most relevant information on the biomedical network.
arXiv Detail & Related papers (2023-11-15T06:34:00Z) - ChatGPT in Drug Discovery: A Case Study on Anti-Cocaine Addiction Drug
Development with Chatbots [5.017265957266848]
The study employs GPT-4 as a virtual guide, offering strategic and methodological insights to researchers working on generative models for drug candidates.
The primary objective is to generate optimal drug-like molecules with desired properties.
This research sheds light on the collaborative synergy between human expertise and AI assistance, wherein ChatGPT's cognitive abilities enhance the development of potential pharmaceutical solutions.
arXiv Detail & Related papers (2023-08-14T03:43:57Z) - 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) - Deep learning for drug repurposing: methods, databases, and applications [54.08583498324774]
Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs.
In this review, we introduce guidelines on how to utilize deep learning methodologies and tools for drug repurposing.
arXiv Detail & Related papers (2022-02-08T09:42:08Z) - 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) - 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) - Applications of artificial intelligence in drug development using
real-world data [3.692950272002333]
The FDA has been actively promoting the use of real-world data (RWD) in drug development.
RWD can generate important real-world evidence reflecting the real-world clinical environment where the treatments are used.
Machine- and deep-learning (ML/DL) methods have been increasingly used across many stages of the drug development process.
arXiv Detail & Related papers (2021-01-22T01:13:54Z)
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.