The Coming Boom In Drug Discovery (original) (raw)
Related papers
Tapping into the drug discovery potential of AI
Biopharma Dealmakers, 2021
In April of this year, the German biotechnology company Evotec announced a phase 1 clinical trial on a new anticancer molecule. The candidate was created in partnership with Exscientia, a company based in Oxford, UK, that applies artificial intelligence (AI) techniques to small-molecule drug discovery. Where it might have taken the traditional discovery process 4-5 years to come up with the drug candidate-an A2 receptor antagonist designed to help T cells fight solid tumors-it was found in 8 months by harnessing Exscientia's 'Centaur Chemist' AI design platform. This system can computationally sort through and compare various properties of millions of potential small molecules, looking for 10 or 20 to synthesize, test and optimize in lab experiments before selecting the eventual drug candidate for clinical trials. Within 3 weeks, Exscientia, a 2012 spinoff of the University of Dundee, UK, announced a 225millionSeriesDfinancinground(Table1),alongwitha225 million Series D financing round (Table 1), along with a 225millionSeriesDfinancinground(Table1),alongwitha300 million equity commitment that it can draw on at its discretion. In 2020, the company did much the same, announcing a drug candidate with Sumitomo Dainippon Pharma in Osaka, Japan, then later raising 100millioninSeriesCfunding.Thatdrug,aselectiveserotoninreuptakeinhibitor(SSRI)designedtotreatobsessivecompulsivedisorder(OCD),andtheoncologydrugarethefirsttwomoleculesdesignedwiththehelpofAItoenterclinicaltrials,Exscientiaclaims.ThecompanyhasalsoformeddrugdiscoverypartnershipswithBristolMyersSquibb(BMS),Sanofi,Bayer,GlaxoSmithKline,RocheandtheUniversityofOxford(Table2),andisbuildingitsownpipeline.ExscientiaisjustoneofmanycompaniesfoundedinthepastdecadearoundAI−basedstrategiesfordrugdiscoveryanddevelopment,severalofwhichhaveraisedsubstantialfundingrecently(Table1).Someofthesearealsodevelopingtoolstoacceleratetheidentificationofsmall−moleculedrugcandidates.OtherssuchasRecursionPharmaceuticals,whichrecentlyraised100 million in Series C funding. That drug, a selective serotonin reuptake inhibitor (SSRI) designed to treat obsessive compulsive disorder (OCD), and the oncology drug are the first two molecules designed with the help of AI to enter clinical trials, Exscientia claims. The company has also formed drug discovery partnerships with Bristol Myers Squibb (BMS), Sanofi, Bayer, GlaxoSmithKline, Roche and the University of Oxford (Table 2), and is building its own pipeline. Exscientia is just one of many companies founded in the past decade around AI-based strategies for drug discovery and development, several of which have raised substantial funding recently (Table 1). Some of these are also developing tools to accelerate the identification of small-molecule drug candidates. Others such as Recursion Pharmaceuticals, which recently raised 100millioninSeriesCfunding.Thatdrug,aselectiveserotoninreuptakeinhibitor(SSRI)designedtotreatobsessivecompulsivedisorder(OCD),andtheoncologydrugarethefirsttwomoleculesdesignedwiththehelpofAItoenterclinicaltrials,Exscientiaclaims.ThecompanyhasalsoformeddrugdiscoverypartnershipswithBristolMyersSquibb(BMS),Sanofi,Bayer,GlaxoSmithKline,RocheandtheUniversityofOxford(Table2),andisbuildingitsownpipeline.ExscientiaisjustoneofmanycompaniesfoundedinthepastdecadearoundAI−basedstrategiesfordrugdiscoveryanddevelopment,severalofwhichhaveraisedsubstantialfundingrecently(Table1).Someofthesearealsodevelopingtoolstoacceleratetheidentificationofsmall−moleculedrugcandidates.OtherssuchasRecursionPharmaceuticals,whichrecentlyraised436 million in Tappin g into the drug discovery potential of AI Plentiful financing and multiple pharma partnerships illustrate the burgeoning interest in applying artificial intelligence tools to drug research and development. Table 1 | Selected recent fi nancings of companies applying AI in drug discovery Company Date Headline Schrödinger February 2020 Drug discovery software company closes 232millionIPObackedbyBillGatesandDavidShaw.InsitroMay2020Insitroraises232 million IPO backed by Bill Gates and David Shaw. Insitro May 2020 Insitro raises 232millionIPObackedbyBillGatesandDavidShaw.InsitroMay2020Insitroraises143 million in Series B funding, to help drive its machine learning-based drug discovery approaches further. AbCellera May 2020 AbCellera raises 105millioninSeriesBfundingroundtoexpanditsantibodydrugdiscoveryplatform.RelayTherapeuticsJuly2020RelayTherapeutics,whichfocusesonunderstandingproteinmotiontodesigndrugcandidates,closes105 million in Series B funding round to expand its antibody drug discovery platform. Relay Therapeutics July 2020 Relay Therapeutics, which focuses on understanding protein motion to design drug candidates, closes 105millioninSeriesBfundingroundtoexpanditsantibodydrugdiscoveryplatform.RelayTherapeuticsJuly2020RelayTherapeutics,whichfocusesonunderstandingproteinmotiontodesigndrugcandidates,closes400 million IPO. Atomwise August 2020 Sanabil Investments co-leads 123millionSeriesBfundingroundforAtomwisetosupportthedevelopmentofitsmoleculeidentificationsoftware.RecursionPharmaceuticalsSeptember2020RecursionPharmaceuticals,whichisapplyingmachinelearningtocellularimagingdata,raises123 million Series B funding round for Atomwise to support the development of its molecule identifi cation software. Recursion Pharmaceuticals September 2020 Recursion Pharmaceuticals, which is applying machine learning to cellular imaging data, raises 123millionSeriesBfundingroundforAtomwisetosupportthedevelopmentofitsmoleculeidentificationsoftware.RecursionPharmaceuticalsSeptember2020RecursionPharmaceuticals,whichisapplyingmachinelearningtocellularimagingdata,raises239 million in Series D fi nancing round led by Bayer's investment department Leaps. Other investors include Casdin Capital, Samsara BioCapital, Baillie Giff ord and Lux Capital. XtalPi September 2020 More than a dozen investment companies raise $318 million in Series C round for start-up XtalPi, which is applying quantum physics with AI to discover drug candidates.
International Journal of Multidisciplinary Sciences and Arts, 2023
The fusion of machine learning (ML) and artificial intelligence (AI) is experiencing a dramatic transition in the field of pharmaceutical research and development. This study examines the several effects of machine learning (ML) on different phases of medication discovery, development, and patient care. The capability of ML to quickly process huge chemical libraries and forecast interactions with target proteins is studied, starting with compound screening and selection. The potential for fewer false positives and negatives, improved hit prediction accuracy, and ensemble technique use are underlined. The part that machine learning plays in enhancing Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profile is then explained. ML models anticipate compound actions inside the human body by analyzing molecular structures and characteristics, improving assessments of drug safety and efficacy. The article goes into further detail about predictive modeling, highlighting how machine learning may be used to find prospective therapeutic targets and confirm their applicability. The combination of multi-omics data, deep learning, and the possibility to identify similar molecular pathways across diseases highlight the game-changing potential of machine learning in this field. The article also covers the use of ML in clinical trials, highlighting its benefits for trial planning, patient recruitment, real-time monitoring, and individualized therapy predictions. By utilizing computational analysis and quantum physics, the power of machine learning-driven de novo drug creation is examined, revealing the potential to develop new therapeutic candidates. In this article, the ethical issues surrounding AI-driven drug discovery are discussed, with a focus on the necessity of transparent data utilization, human oversight, and responsible data consumption. The report ends by predicting ML's potential for pharmaceutical R&D in the future. Accelerated drug discovery pipelines, the rise of customized medicine powered by predictive models, optimized clinical trials, and a change in medication repurposing tactics are all envisaged in this. The report emphasizes the revolutionary potential of ML in altering pharmaceutical research and development while noting obstacles in data quality, model interpretability, ethics, and interdisciplinary collaboration. It is suggested that the ethical integration of AI technologies, interdisciplinary cooperation, and regulatory modifications are essential steps to unlock the full potential of ML and AI and, ultimately, provide patients throughout the world with safer, more efficient, and individualized treatments.
Application of Machine Learning in Drug Discovery and Development Lifecycle
Machine learning and Artificial Intelligence have significantly advanced in recent years owing to their potential to considerably increase the quality of life while reducing human workload. The paper demonstrates how AI and ML are used in the drug development process to shorten and enhance the overall timeline. It contains pertinent information on a variety of Machine Learning approaches and algorithms that are used across the whole drug development process to speed up research, save expenses, and reduce risks related to clinical trials. A range of QSAR analysis, hit finding, and de novo drug design applications are used in the pharmaceutical industry to enhance decision-making. As technologies like high-throughput screening and computation analysis of databases used for lead and target identification and development create and integrate vast volumes of data, machine learning and deep learning have grown in importance. It has also been emphasized how these cognitive models and tools may be used in lead creation, optimization, and thorough virtual screening. In this paper, problem statements and the corresponding state-of-the-art models have been considered for target validation, prognostic biomarkers, and digital pathology. Machine Learning models play a vital role in the various operations related to clinical trials embracing protocol optimization, participant management, data analysis and storage, clinical trial data verification, and surveillance. Post-development drug monitoring and unique industrially prevalent ML applications of pharmacovigilance have also been discussed. As a result, the goal of this study is to investigate the machine learning and deep learning algorithms utilised across the drug development lifecycle as well as the supporting techniques that have the potential to be useful.
ACCELERATING PHARMACEUTICAL INNOVATION: THE IMPACT OF AI ON DRUG DISCOVERY
IAEME PUBLICATION, 2024
The integration of artificial intelligence (AI) and machine learning technologies in drug discovery has the potential to revolutionize the pharmaceutical industry. Traditional drug discovery processes are time-consuming, costly, and often inefficient, with the average drug development timeline spanning over a decade and costs exceeding billions of dollars. However, AI-powered approaches offer a transformative solution by efficiently analyzing vast amounts of data, predicting molecular interactions, and identifying promising drug candidates. This article explores the various methodologies employed in AI-driven drug discovery, including deep learning models, virtual screening, molecular docking, and generative models. The results section talks about several successful case studies that show how AI can speed up the search for new therapeutic agents. For example, powerful inhibitors for cancer-related enzymes were found, and drug candidates for obsessive-compulsive disorder and antibiotic-resistant bacteria were made very quickly. Despite the promising results, challenges such as data quality, model interpretability, and experimental validation need to be addressed to fully realize the potential of AI in drug discovery.
Investigating the Impact of Machine Learning in Pharmaceutical Industry
Journal of Pharmaceutical Research International
In the pharmaceutical and consumer health industries, artificial intelligence and machine learning played an important role. These technologies are critical for the identification of patients with improved intelligence applications, such as disease detection and diagnostics for clinical testing, for medicine production and predictive forecasts. In recent years, advances in numerous analysis tools and machine learning algorithms have led to novel applications for machine learning in several areas of pharmaceutical science. This paper examines the past, present, and future impacts of machine learning on several areas, including medicine design and discovery. Artificial neural networks are employed in pharmaceutical machine learning because they can reproduce nonlinear interactions typical in pharmaceutical research. AI and learning machines are examined in everyday pharmaceutical needs, industrial and regulatory insights.
Artificial intelligence in drug discovery
Future medicinal chemistry, 2018
While many of the new approaches have yet to bear fruit in terms of drugs being progressed to market, initial reports tend toward the belief that they will become even more integral in the drug discovery process than has hitherto been seen. "
New-Generation Drug Discovery using Machine Learning
International Journal for Research in Applied Science and Engineering Technology, 2023
Finding innovative molecules with specific chemical properties to treat diseases is one of the goals of drug discovery. Recent years have seen the production of a sizable volume of biological data from many sources. These statistics and molecular analyses have been used to determine the most effective medications. Medical research often frustrates people and is far more expensive. The work at hand is made easier by having the ability to predict whether a medicine will be active or not. The information about the drug can also be used to develop other drugs. One application that makes use of machine learning to enhance decision-making in pharmaceutical data across numerous applications is quantitative structure activity relationship (QSAR) analysis. Machine learning-based predictive models have recently gained a lot of attention in areas outside of preclinical research. Costs and research times associated with finding new drugs are considerably decreased at this stage. Drug research is growing and more commonly utilising machine learning, algorithms for pattern recognition, knowledge of mathematical correlations, and knowledge of the chemical and biological characteristics of molecules. The necessity for a sizable volume of data, the incapacity to interpret the data, and other issues are further restrictions. Without the need for computational resources, massive amounts of data can be analysed using both physical models and machine learning approaches.
2020
Over the past few years, pharmaceutical R&D has become aware of the potential benefits of leveraging artificial intelligence and its collective subfields including machine learning, deep learning, data science and advanced analytics. These technologies are being embraced across industries to provide enhanced automation, gain insights into data, and improve data-driven decision making. The evangelization from lower level technical experts has now been echoed by the top levels of many organizations, as exemplified by Vas Narasimhan’s (Novartis CEO) goal to evolve AI to place it at the “heart of the company” [1] and Alex Bourla’s (Pfizer CEO) aim to win the digital race in pharma using machine learning and AI to expedite R&D [2]. Although its value compared to pure science continues to be questioned, machine learning and particularly deep learning have introduced many compelling use cases.
Revolutionizing Drug Discovery: Harnessing Machine Learning Algorithms
International Journal For Multidisciplinary Research, 2024
Drug discovery is a crucial element of biomedical research, with the goal of finding and creating new medical treatments for a variety of illnesses. Yet, the conventional process of finding new drugs is frequently impeded by its intrinsic difficulties, such as expensive expenses, long durations, and poor success rates in trials with patients. Recently, the incorporation of machine learning (ML) algorithms has become a revolutionary method to streamline and improve different phases of drug discovery. This summary offers a glimpse into the rapidly growing area of drug discovery using machine learning algorithms, emphasizing its potential to transform the process of developing treatments. The usual process of discovering drugs involves various stages such as identifying the target, finding lead compounds, conducting preclinical tests, undergoing clinical trials, and obtaining regulatory approval. All these phases require a lot of labor, time, and resources, leading to high attrition rates and limited success in turning potential compounds into approved therapies. Nevertheless, researchers can enhance and speed up crucial parts of the drug discovery process by using ML algorithms. ML algorithms use data to aid in drug discovery by utilizing computational models to examine large quantities of biological, chemical, and clinical data. These algorithms can learn from various types of data, such as genomic data, chemical structures, protein interactions, and clinical outcomes, to discover hidden patterns, find new targets for drugs, and forecast the effectiveness and safety of potential treatments. Moreover, machine learning algorithms allow for the investigation of intricate connections between molecular structures and biological effects, making it easier to create improved drug candidates with better effectiveness and specificity. Important uses of machine learning in pharmaceutical research involve finding and confirming targets, screening compounds and improving leads, repurposing drugs, and tailoring treatments for individuals. Commonly used for classification and regression tasks, supervised learning algorithms like support vector machines and random forests predict compound activity, toxicity, and pharmacokinetic properties. Clustering and dimensionality reduction techniques utilized in unsupervised learning algorithms help analyze vast datasets and discover new drug-target interactions. Advanced abilities for analyzing molecular structures, virtual screening, and designing new drugs are provided by deep learning models like convolutional neural networks and recurrent neural networks. Multiple case studies demonstrate how ML algorithms can significantly impact drug discovery. Collaboration among academia, industry, and research institutions has resulted in the creation of new MLbased methods for drug development, identifying targets, and categorizing patients. Nevertheless, there are challenges accompanying the widespread use of ML in drug discovery. In healthcare, it is crucial to address ethical considerations, regulatory hurdles, and data privacy concerns to ensure the responsible and ethical use of ML algorithms.