AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-Scale Examination of Bio-Pharma Firms

MIS Quarterly ◽  
2021 ◽  
Vol 45 (3) ◽  
pp. 1451-1482
Author(s):  
Bowen Lou ◽  
◽  
Lynn Wu ◽  
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Drug Development ◽  
Large Scale ◽  
New Drugs ◽  
Innovation Capability ◽  
Domain Experts ◽  
Medium Level ◽  
Job Postings ◽  
Mechanism Of Impact

Advances in artificial intelligence (AI) could potentially reduce the complexities and costs in drug discovery. We conceptualize an AI innovation capability that gauges a firm’s ability to develop, manage, and utilize AI resources for innovation. Using patents and job postings to measure AI innovation capability, we find that it can affect a firm’s discovery of new drug-target pairs for preclinical studies. The effect is particularly pronounced for developing new drugs whose mechanism of impact on a disease is known and for drugs at the medium level of chemical novelty. However, AI is less helpful in developing drugs when there is no existing therapy. AI is also less helpful for drugs that are either entirely novel or those that are incremental “follow-on” drugs. Examining AI skills, a key component of AI innovation capability, we find that the main effect of AI innovation capability comes from employees possessing the combination of AI skills and domain expertise in drug discovery as opposed to employees possessing AI skills only. Having the combination is key because developing and improving AI tools is an iterative process requiring synthesizing inputs from both AI and domain experts during both the development and the operational stages of the tool. Taken together, our study sheds light on both the advantages and the limitations of using AI in drug discovery and how to effectively manage AI resources for drug development.

scholarly journals Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Nagasundaram Nagarajan ◽  
Edward K. Y. Yapp ◽  
Nguyen Quoc Khanh Le ◽  
Balu Kamaraj ◽  
Abeer Mohammed Al-Subaie ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Computational Biology ◽  
New Drugs ◽  
Pharmaceutical Companies ◽  
Data Sets ◽  
Aggregated Data ◽  
Usable Knowledge ◽  
Knowledge Of Cancer ◽  
Generation Sequencing

Artificial intelligence (AI) proves to have enormous potential in many areas of healthcare including research and chemical discoveries. Using large amounts of aggregated data, the AI can discover and learn further transforming these data into “usable” knowledge. Being well aware of this, the world’s leading pharmaceutical companies have already begun to use artificial intelligence to improve their research regarding new drugs. The goal is to exploit modern computational biology and machine learning systems to predict the molecular behaviour and the likelihood of getting a useful drug, thus saving time and money on unnecessary tests. Clinical studies, electronic medical records, high-resolution medical images, and genomic profiles can be used as resources to aid drug development. Pharmaceutical and medical researchers have extensive data sets that can be analyzed by strong AI systems. This review focused on how computational biology and artificial intelligence technologies can be implemented by integrating the knowledge of cancer drugs, drug resistance, next-generation sequencing, genetic variants, and structural biology in the cancer precision drug discovery.

Survey of Machine Learning Techniques in Drug Discovery

2019 ◽  
Vol 20 (3) ◽  
pp. 185-193 ◽  
Author(s):  
Natalie Stephenson ◽  
Emily Shane ◽  
Jessica Chase ◽  
Jason Rowland ◽  
David Ries ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
State Of The Art ◽  
New Drugs ◽  
Machine Learning Techniques ◽  
Current Status ◽  
Learning Techniques ◽  
Pharmaceutical Industries ◽  
Current Stage

Background:Drug discovery, which is the process of discovering new candidate medications, is very important for pharmaceutical industries. At its current stage, discovering new drugs is still a very expensive and time-consuming process, requiring Phases I, II and III for clinical trials. Recently, machine learning techniques in Artificial Intelligence (AI), especially the deep learning techniques which allow a computational model to generate multiple layers, have been widely applied and achieved state-of-the-art performance in different fields, such as speech recognition, image classification, bioinformatics, etc. One very important application of these AI techniques is in the field of drug discovery.Methods:We did a large-scale literature search on existing scientific websites (e.g, ScienceDirect, Arxiv) and startup companies to understand current status of machine learning techniques in drug discovery.Results:Our experiments demonstrated that there are different patterns in machine learning fields and drug discovery fields. For example, keywords like prediction, brain, discovery, and treatment are usually in drug discovery fields. Also, the total number of papers published in drug discovery fields with machine learning techniques is increasing every year.Conclusion:The main focus of this survey is to understand the current status of machine learning techniques in the drug discovery field within both academic and industrial settings, and discuss its potential future applications. Several interesting patterns for machine learning techniques in drug discovery fields are discussed in this survey.

scholarly journals An expanding role for cell biologists in drug discovery and pharmacology

2012 ◽  
Vol 23 (21) ◽  
pp. 4162-4164 ◽  
Author(s):  
Peter K. Sorger ◽  
Birgit Schoeberl
Keyword(s):  
Drug Discovery ◽  
Pharmaceutical Industry ◽  
Drug Development ◽  
Basic Structure ◽  
Healthcare Systems ◽  
New Drugs ◽  
Conceptual Basis ◽  
The Face ◽  
Programming Skills ◽  
In Cells

The profound challenges facing clinicians, who must prescribe drugs in the face of dramatic variability in response, and the pharmaceutical industry, which must develop new drugs despite ever-rising costs, represent opportunities for cell biologists interested in rethinking the conceptual basis of pharmacology and drug discovery. Much better understanding is required of the quantitative behaviors of networks targeted by drugs in cells, tissues, and organisms. Cell biologists interested in these topics should learn more about the basic structure of drug development campaigns and hone their quantitative and programming skills. A world of conceptual challenges and engaging industry–academic collaborations awaits, all with the promise of delivering real benefit to patients and strained healthcare systems.

scholarly journals Natural Products-Derived Chemicals: Breaking Barriers to Novel Anti-HSV Drug Development

Viruses ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 154 ◽  
Author(s):  
Jakub Treml ◽  
Markéta Gazdová ◽  
Karel Šmejkal ◽  
Miroslava Šudomová ◽  
Peter Kubatka ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Natural Products ◽  
Drug Development ◽  
Large Scale ◽  
Biologically Active ◽  
New Drugs ◽  
In Vivo Studies ◽  
Limited Attention ◽  
Hsv Infections

Recently, the problem of viral infection, particularly the infection with herpes simplex virus type 1 (HSV-1) and type 2 (HSV-2), has dramatically increased and caused a significant challenge to public health due to the rising problem of drug resistance. The antiherpetic drug resistance crisis has been attributed to the overuse of these medications, as well as the lack of new drug development by the pharmaceutical industry due to reduced economic inducements and challenging regulatory requirements. Therefore, the development of novel antiviral drugs against HSV infections would be a step forward in improving global combat against these infections. The incorporation of biologically active natural products into anti-HSV drug development at the clinical level has gained limited attention to date. Thus, the search for new drugs from natural products that could enter clinical practice with lessened resistance, less undesirable effects, and various mechanisms of action is greatly needed to break the barriers to novel antiherpetic drug development, which, in turn, will pave the road towards the efficient and safe treatment of HSV infections. In this review, we aim to provide an up-to-date overview of the recent advances in natural antiherpetic agents. Additionally, this paper covers a large scale of phenolic compounds, alkaloids, terpenoids, polysaccharides, peptides, and other miscellaneous compounds derived from various sources of natural origin (plants, marine organisms, microbial sources, lichen species, insects, and mushrooms) with promising activities against HSV infections; these are in vitro and in vivo studies. This work also highlights bioactive natural products that could be used as templates for the further development of anti-HSV drugs at both animal and clinical levels, along with the potential mechanisms by which these compounds induce anti-HSV properties. Future insights into the development of these molecules as safe and effective natural anti-HSV drugs are also debated.

Recent Advances in System Based Study for Anti-Malarial Drug Development Process

2019 ◽  
Vol 25 (31) ◽  
pp. 3367-3377 ◽  
Author(s):  
Brijesh S. Yadav ◽  
Navaneet Chaturvedi ◽  
Ninoslav Marina
Keyword(s):  
Drug Discovery ◽  
Large Scale ◽  
New Drugs ◽  
The Novel ◽  
Sequencing Data ◽  
Drug Molecules ◽  
Genes Expression ◽  
Depth Analysis ◽  
New Generation Sequencing ◽  
New Generation

Background: Presently, malaria is one of the most prevalent and deadly infectious disease across Africa, Asia, and America that has now started to spread in Europe. Despite large research being carried out in the field, still, there is a lack of efficient anti-malarial therapeutics. In this paper, we highlight the increasing efforts that are urgently needed towards the development and discovery of potential antimalarial drugs, which must be safe and affordable. The new drugs thus mentioned are also able to counter the spread of malaria parasites that have been resistant to the existing agents. Objective: The main objective of the review is to highlight the recent development in the use of system biologybased approaches towards the design and discovery of novel anti-malarial inhibitors. Method: A huge literature survey was performed to gain advance knowledge about the global persistence of malaria, its available treatment and shortcomings of the available inhibitors. Literature search and depth analysis were also done to gain insight into the use of system biology in drug discovery and how this approach could be utilized towards the development of the novel anti-malarial drug. Results: The system-based analysis has made easy to understand large scale sequencing data, find candidate genes expression during malaria disease progression further design of drug molecules those are complementary of the target proteins in term of shape and configuration. Conclusion: The review article focused on the recent computational advances in new generation sequencing, molecular modeling, and docking related to malaria disease and utilization of the modern system and network biology approach to antimalarial potential drug discovery and development.

scholarly journals SMALL MOLECULES SOLVING BIG PROBLEMS: PRESENT AND FUTURE OF DRUG DISCOVERY

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Anna Lucia Fallacara ◽  
Iuni Margaret Laura Tris ◽  
Amalia Belfiore ◽  
Maurizio Botta
Keyword(s):  
Drug Discovery ◽  
Drug Development ◽  
Small Molecules ◽  
Rational Design ◽  
Development Process ◽  
New Drugs ◽  
Drug Development Process ◽  
Development Models ◽  
The Past ◽  
The Cost

The Drug development process has undergone a great change over the years. The way, from haphazard discovery of new natural products with a potent biological activity to a rational design of small molecule effective against a selected target, has been long and sprinkled with difficulties. The oldest drug development models are widely perceived as opaque and inefficient, with the cost of research and development continuing to rise even if the production of new drugs remains constant. The present paper, will give an overview of the principles, approaches, processes, and status of drug discovery today with an eye towards the past and the future.

scholarly journals Complexity Theory: Artificial Intelligence System Help Safety Improvement in the Next Pandemic

2020 ◽  
Vol 5 (09) ◽  
pp. 362-368
Author(s):  
Shahad Faisal Halabi
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Complexity Theory ◽  
High Performance ◽  
New Drugs ◽  
Hospital Environment ◽  
Western World ◽  
Intelligence System ◽  
Safety Improvement ◽  
The Impact

As the coronavirus pandemic spread from Asia to the western world, drug discovery came to a near standstill. Most laboratories shut down and instruments and reagents were left untouched, except for the most essential work. The pandemic forced large and small companies, regulatory and government agencies, and academia to tap into technology, particularly artificial intelligence (AI) and machine learning (ML), for providing more than just speed and efficiency. This essay aims to dig deeply in complexity theory to help improve safety and reduce the impact of the next pandemic. It is based on implementing Artificial Intelligence (AI) to provide the safer complex theory with an example of the current situation of COVID-19. While there are no shortcuts around scientific rigor and experimentation, AI can certainly accelerate the discovery of new drugs particularly when combined with high-performance computing (HPC) and quantum computing. Evaluating new AI technologies, particularly in areas of drug discovery where there are few demonstrations of success, can be a real challenge. It is considered that safety improvement of alert systems and the risk factors, in order to organize the safety of health facilities and control the hospital environment before the potential pandemic develops. Here, we will try to apply complexity theory in our dealing with future pandemics based on the situation analysis of previous experiences.

scholarly journals Strategies and Techniques of Drug Discovery from Natural Products

2017 ◽  
Vol II (I) ◽  
pp. 34-43
Author(s):  
Parniya Akbar Ali ◽  
Farah Hanif ◽  
Hosna Nettour ◽  
Mubashar Rehman
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Drug Development ◽  
Positive Impact ◽  
New Drugs ◽  
Active Materials ◽  
Natural Sources ◽  
Innovative Strategies ◽  
Therapeutic Properties ◽  
New Drug Development

New drugs are mostly obtained from Natural sources. The traditional and ethic medicines have provided evidence on the therapeutic properties and resulted in some distinguished drug discovery of natural products. The microorganisms and the endogenous active materials from human or animal have also become a significant approach to the discovery of a drug. Bioinformatics and artificial intelligence have facilitated the study and development of products. For discovery of natural products different software have been used. Different computational software needed in the future for the predicting features in new drug development, for instance pharmacokinetic and pharmacodynamics, in drug development lead positive impact. This review focus on natural product drug discovery and uses innovative strategies and techniques as a part of discovery of drugs from natural products.

Proposed Standards and Regulatory Mechanisms for New Homeopathic Drug Development

2021 ◽  
Vol 34 (04) ◽  
pp. 283-290
Author(s):  
Gitanjali Talele ◽  
Rajesh Shah
Keyword(s):  
Drug Discovery ◽  
Drug Development ◽  
Regulatory Mechanism ◽  
New Drugs ◽  
Regulatory Mechanisms ◽  
Regulatory Body ◽  
Market Authorisation ◽  
New Drug ◽  
Homeopathic Drug ◽  
Basic Proposition

Abstract Introduction Researchers working with new insights and new targets in new drug discovery in the homeopathy space observe that the path of drug-development and market authorisation has been less travelled and the pathway is not yet well-mapped. The need of the time is to define clear guidelines and regulatory mechanisms to facilitate the process of new drug discovery. Overview The article is about the proposed methods for identifying the new homeopathic substances for therapeutic use. An overview of the current regulations for drug development in India is discussed in this article. Method of new drug development in homeopathy, standards and regulatory mechanism for approval of new drugs are proposed with few illustrations and references. An introductory plan, based on the perspective and experience of researcher, practitioner, academician and inventor for drug discovery is proposed. Discussion An urgent need for setting up the guidelines for new drug discovery has been identified and a basic proposition is made for the same, suggesting practical, pragmatic and achievable measures, and independent regulatory body to encourage drug development and research.

scholarly journals Emergence of Chinese Drug Discovery Research

2014 ◽  
Vol 20 (3) ◽  
pp. 318-329 ◽  
Author(s):  
Caihong Zhou ◽  
Yan Zhou ◽  
Jia Wang ◽  
Yue Zhu ◽  
Jiejie Deng ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Large Scale ◽  
New Drugs ◽  
Chinese Drug ◽  
Academic Institutions ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Class B ◽  
Biotechnology Companies ◽  
Glucagon Like Peptide 1

The identification of hits and the generation of viable leads is an early and yet crucial step in drug discovery. In the West, the main players of drug discovery are pharmaceutical and biotechnology companies, while in China, academic institutions remain central in the field of drug discovery. There has been a tremendous amount of investment from the public as well as private sectors to support infrastructure buildup and expertise consolidation relative to drug discovery and development in the past two decades. A large-scale compound library has been established in China, and a series of high-impact discoveries of lead compounds have been made by integrating information obtained from different technology-based strategies. Natural products are a major source in China’s drug discovery efforts. Knowledge has been enhanced via disruptive breakthroughs such as the discovery of Boc5 as a nonpeptidic agonist of glucagon-like peptide 1 receptor (GLP-1R), one of the class B G protein–coupled receptors (GPCRs). Most of the original hit identification and lead generation were carried out by academic institutions, including universities and specialized research institutes. The Chinese pharmaceutical industry is gradually transforming itself from manufacturing low-end generics and active pharmaceutical ingredients to inventing new drugs.

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