{"id":2148,"date":"2025-06-23T13:21:37","date_gmt":"2025-06-23T13:21:37","guid":{"rendered":"https:\/\/leyton.majjane.agency\/ca\/post\/artificial-intelligence-drug-discovery-innovation-in-pharmacy\/"},"modified":"2026-07-26T17:03:09","modified_gmt":"2026-07-26T15:03:09","slug":"artificial-intelligence-drug-discovery-innovation-in-pharmacy","status":"publish","type":"article","link":"https:\/\/leyton.com\/ca\/en\/insights\/articles\/artificial-intelligence-drug-discovery-innovation-in-pharmacy\/","title":{"rendered":"Artificial Intelligence Drug Discovery: Innovation in Pharmacy"},"content":{"rendered":"<p>Great minds always collaborate on problem-solving, and the <strong><a href=\"https:\/\/leyton.com\/ca\/insights\/articles\/pharmaceutical-industry-in-pursuit-of-rd\/\" target=\"_blank\" rel=\"noreferrer noopener\">pharmaceutical industry<\/a> <\/strong>is the most prominent representation of this in relation to <strong>drug discovery<\/strong>. A critical factor in <strong><a href=\"https:\/\/leyton.com\/ca\/insights\/articles\/pharmaceutical-industry-in-pursuit-of-rd\/\">pharmaceutical research and development<\/a><\/strong>.<\/p>\n<p><strong>Innovation <\/strong>of this type takes planning and involves many moving parts. It requires extensive collaboration between <strong><a href=\"https:\/\/leyton.com\/ca\/chemistry-companies-driving-innovation-for-a-better-tomorrow\/\">chemistry<\/a><\/strong>, biology, toxicology, and pharmacokinetics to bring about one viable candidate to cure any disease.  <\/p>\n<p>Fortunately, scientists are not thrown into the deep end to evaluate all these factors manually, as they used to in earlier decades. <\/p>\n<p>The role of <strong><a href=\"https:\/\/leyton.com\/ca\/insights\/articles\/artificial-intelligence-use-applications-and-impact-in-the-services-sector\/\">Artificial Intelligence<\/a> in drug discovery<\/strong> is increasing, and according to McKinsey, incorporating AI capabilities into big data strategies has the potential to generate an annual value of up to\u202f<a href=\"https:\/\/www.mckinsey.com\/industries\/life-sciences\/our-insights\/how-big-data-can-revolutionize-pharmaceutical-r-and-d\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>$100 billion<\/strong>\u202f<\/a>within the US healthcare system. <\/p>\n<p>This involves using predictive modelling and performing a thorough analysis of sensor data.&nbsp;<\/p>\n<h2 class=\"wp-block-heading\">What is Artificial Intelligence?&nbsp;&nbsp;<\/h2>\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/leyton.com\/ca\/wp-content\/blogs.dir\/3\/files\/2025\/05\/AI-in-drug-discorvery-pharmaceutical-field-1-1024x349.jpg\" alt=\"Team of Computer Engineers Work on Machine Learning Neural Network Technology Development\" class=\"wp-image-35586\" \/><\/figure>\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<p>The <a href=\"https:\/\/www.fda.gov\/medical-devices\/software-medical-device-samd\/artificial-intelligence-and-machine-learning-software-medical-device#whatis\"><strong>US Food and Drug Administration (FDA)<\/strong><\/a> defines <strong><a href=\"https:\/\/leyton.com\/ca\/insights\/articles\/how-automation-and-ai-are-changing-the-way-we-work\/\">AI<\/a> <\/strong>as \u201c<em>the science and engineering of making intelligent machines<\/em>\u201d.<\/p>\n<p>While <a href=\"https:\/\/leyton.com\/ca\/insights\/articles\/the-sred-tax-credit-in-machine-learning\/\"><strong>Machine Learning<\/strong><\/a> is \u201can AI technique used to design and train software algorithms to learn from data. <\/p>\n<p>It\u2019s important to note that <strong>all <a href=\"https:\/\/leyton.com\/ca\/insights\/articles\/ml-models-for-stock-market-prediction\/\">machine learning (ML) techniques<\/a><\/strong> are considered <strong>AI techniques<\/strong>, but <strong>not all AI techniques <\/strong>involve <strong>ML<\/strong>. <\/p>\n<p>Standard <strong><a href=\"https:\/\/leyton.com\/ca\/insights\/articles\/the-future-of-artificial-intelligence\/\">AI <\/a><\/strong>workflows entail the following steps:<\/p>\n<ul>\n<li>Formulating a problem<\/li>\n<li>Preparing data<\/li>\n<li>Extracting features<\/li>\n<li>Selecting training<\/li>\n<li>Testing datasets<\/li>\n<li>Developing a model<\/li>\n<li>Training the model and testing its performance (cross-validation)<\/li>\n<li>Applying the model to testing datasets<\/li>\n<li>Refining the model<\/li>\n<\/ul>\n<div style=\"height:33px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<p>They also involve <strong>numerous algorithms<\/strong> and <strong>statistical modelling techniques <\/strong>that require in-depth knowledge from developers.  <\/p>\n<h2 class=\"wp-block-heading\">How hard is it to create a new drug?&nbsp;<\/h2>\n<p>The short answer is : it\u2019s not easy. But recent technological advances have made <a href=\"https:\/\/leyton.com\/ca\/insights\/articles\/sred-in-the-pharmaceutical-industry\/\"><strong>pharmaceutical research and development<\/strong><\/a> more accessible and efficient. <\/p>\n<p>To study each disease, scientists need to examine the<strong> receptors, enzymes, proteins, <\/strong>and <strong>genes <\/strong>associated with the <strong>disease<\/strong>. Once evaluated, the process of formulating a drug or treatment can begin. The diagram below shows the required drug discovery steps courtesy of <a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC8043990\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Vatansever et al., 2021<\/strong><\/a>.  <\/p>\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<figure class=\"wp-block-image aligncenter size-full is-resized\"><img decoding=\"async\" src=\"https:\/\/leyton.com\/ca\/wp-content\/blogs.dir\/3\/files\/2025\/05\/image.png\" alt=\"Stages of drug development that AI or ML approaches can be applied to speed up the research process.  \" class=\"wp-image-35488\" style=\"width:421px;height:auto\" title=\"Stages of drug development that AI or ML approaches can be applied to speed up the research process.  \" \/><figcaption class=\"wp-element-caption\"><em>Stages of drug development that AI or ML approaches can be applied to speed up the research process.&nbsp;&nbsp;<\/em><\/figcaption><\/figure>\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<p>The central aspect of <strong>drug discovery<\/strong> is to design a <strong>molecule <\/strong>that can <strong>reverse a disease<\/strong> by changing the activity of a target. This is why target identification is the first step in the process.<\/p>\n<p>A good drug target needs to be relevant to the disease <strong>phenotype <\/strong>and suitable for<strong> therapeutic modulation <\/strong>(\u201c<strong>druggable<\/strong>\u201d).<\/p>\n<p>Researchers also need to ensure the drug has a therapeutic benefit<strong> within an acceptable safety margin<\/strong>.<\/p>\n<p>A scientist may realize that a drug is affecting the intended target but will still need to evaluate the mode of action to ensure patient safety using cell models, animal models, and, eventually, patient trials.&nbsp;<\/p>\n<h2 class=\"wp-block-heading\">How is AI being used in drug development?&nbsp;<\/h2>\n<p>The analysis of large\u2010scale <strong>multidimensional biological data<\/strong> requires effective methods to produce accurate predictions for <strong>target identification<\/strong>.<\/p>\n<p><strong><a href=\"https:\/\/leyton.com\/ca\/insights\/articles\/how-ai-projects-are-changing-the-landscape\/\">AI<\/a><\/strong> is a<strong> robust technology<\/strong> used for analyzing the rapidly <strong>increasing multi-omics data <\/strong>in the <strong>identification of potential therapeutic targets<\/strong>.<\/p>\n<p>In this section, we will discuss <strong>AI <\/strong>and <strong>drug discovery<\/strong>.  <\/p>\n<h3 class=\"wp-block-heading\">Target prioritization<\/h3>\n<p>Nominating new drug targets and validating them can be expensive and time-consuming. Here, <strong>AI <\/strong>has been instrumental.<\/p>\n<p>One ML method that has been applied to <strong>disease targets <\/strong>is <strong>SVM<\/strong>, which has historically been used to recognize handwriting, detect faces or identify a speaker.<\/p>\n<p><strong><a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC4143549\/\" target=\"_blank\" rel=\"noreferrer noopener\">Jeon et al. 2014<\/a><\/strong> built an <strong>SVM classifier<\/strong> that uses features from various data types (DNA copy number, messenger RNA expression, mutation occurrence, and PPI) to prioritize <strong>drug targets <\/strong>specific to pancreatic, breast and ovarian cancers to <strong>identify <\/strong>and <strong>prioritize novel cancer drug targets<\/strong>.<\/p>\n<p>The <strong>SVM algorithm<\/strong> is powerful, and they anticipate developing<strong> new kernel functions <\/strong>that can help in the discovery of new targets for<strong> heterogeneous cancers<\/strong>, such as triple-negative breast cancers (TNBCs) and soft tissue sarcomas (STS). <\/p>\n<h3 class=\"wp-block-heading\">Druggability <\/h3>\n<p>The likelihood of influencing a specific target with a <strong>small-molecule drug<\/strong> which depends on the target&#8217;s ability to bind small molecules based on its biophysical features.<\/p>\n<p><strong>AI drug discovery<\/strong> companies have addressed <strong>druggability<\/strong> by training models to estimate it using different properties. Including geometric, structural, and physicochemical features of drug\u2010binding and nondrug\u2010binding cavities on proteins.<\/p>\n<p>This has helped researchers find that the most critical attributes to estimate druggability are the size and shape of the surface cavities of the protein. <\/p>\n<p>For example: <a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC3045802\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Costa et al., 2010<\/strong><\/a> developed a decision tree-based meta-classifier by training on attributes such as network topological features, tissue expression profiles, and subcellular localization for each <strong>druggable <\/strong>and <strong>non-druggable gene<\/strong>.<\/p>\n<p>It <strong>correctly identified 65% <\/strong>of <strong>known morbid genes<\/strong> with a <strong>precision of 66%.<\/strong> And c<strong>orrectly identified 78%<\/strong> of <strong>known druggable genes<\/strong> with a <strong>precision of 75%<\/strong>. This field still requires innovation. <\/p>\n<h3 class=\"wp-block-heading\">Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADME\u2010T) predictions<\/h3>\n<p><strong>ADME\u2010T<\/strong> properties are responsible for approximately half of all clinical failures, which is why it is essential to improve this process with <strong>AI <\/strong>applications.<\/p>\n<p>Most prediction models attempt to build a direct relationship between molecular descriptors and <strong>ADME <\/strong>properties.<\/p>\n<p>The introduction of <strong>capsule networks<\/strong>,\u202fa machine learning system that is a type of <strong>deep neural network\u202f(DNN)<\/strong> that can be used to model hierarchical relationships, has improved <strong>ADME\u2010T prediction<\/strong>.<\/p>\n<p>For example, to predict the cardiotoxicity of drugs, <a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC6997788\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Wang et al. 2019<\/strong><\/a> developed two capsule network architectures: onvolution\u2010capsule network\u202f(Conv\u2010CapsNet) and restricted Boltzmann machine\u2010capsule network (RBM\u2010CapsNet) with 91.8% and 92.2% accuracy respectively.&nbsp;<\/p>\n<h2 class=\"wp-block-heading\">Need Pharmaceutical R&amp;D Funding? Leyton can help<\/h2>\n<p><a href=\"https:\/\/leyton.com\/ca\/insights\/articles\/pharmaceutical-industry-in-pursuit-of-rd\/\"><strong>Pharmaceutical research and development<\/strong><\/a> is experiencing declining success rates and a stagnant pipeline.<\/p>\n<p>Facilitating this type of innovation can be costly and time-consuming while posing great risk for <strong><a href=\"https:\/\/leyton.com\/ca\/videos\/sred-in-the-pharmaceutical-industry-what-you-need-to-know\/\">pharmaceutical and AI drug discovery companies<\/a><\/strong>.<\/p>\n<p>If you need<a href=\"https:\/\/leyton.com\/ca\/leveraging-financial-incentives-to-support-the-life-sciences-sector\/\"> <strong>funding for using Artificial Intelligence in drug discovery<\/strong><\/a>, <a href=\"https:\/\/leyton.com\/ca\/en\/\"><strong>Leyton <\/strong><\/a>has access to infinite grants that can help you cover the costs of this work.  <\/p>\n<div class=\"wp-block-buttons\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/leyton.com\/ca\/contact-us\/\">Contact an expert now!<\/a><\/div>\n<\/div>\n<div style=\"height:70px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<h3>Sources<\/h3>\n<ul>\n<li><strong>How Big Data can revolutionize pharmaceutical R&amp;D &#8211; Mckinsey <\/strong><a href=\"https:\/\/www.mckinsey.com\/industries\/life-sciences\/our-insights\/how-big-data-can-revolutionize-pharmaceutical-r-and-d\">https:\/\/www.mckinsey.com\/industries\/life-sciences\/our-insights\/how-big-data-can-revolutionize-pharmaceutical-r-and-d<\/a><\/li>\n<li><strong>Artificial Intelligence and Machine Learning in Software as a Medical Device &#8211; US Food &amp; Drug <\/strong><a href=\"https:\/\/www.fda.gov\/medical-devices\/software-medical-device-samd\/artificial-intelligence-and-machine-learning-software-medical-device#whatis\">https:\/\/www.fda.gov\/medical-devices\/software-medical-device-samd\/artificial-intelligence-and-machine-learning-software-medical-device#whatis<\/a><\/li>\n<li><strong>Artificial intelligence and machine learning\u2010aided drug discovery in central nervous system diseases: State\u2010of\u2010the\u2010arts and future directions<\/strong> <strong>&#8211; PMC NCBI <\/strong><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC8043990\/\">https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC8043990\/<\/a><\/li>\n<li><strong>A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening &#8211; PMC NCBI <\/strong><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC4143549\/\">https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC4143549\/<\/a><\/li>\n<li><strong>A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data &#8211; PMC NCBI <\/strong> <a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC4143549\/\">https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC4143549\/<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Great minds always collaborate on problem-solving, and the pharmaceutical industry is the most prominent representation of this in relation to drug discovery. A critical factor in pharmaceutical research and development. Innovation of this type takes planning and involves many moving parts. It requires extensive collaboration between chemistry, biology, toxicology, and pharmacokinetics to bring about one [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":2847,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[126,286,537,309],"tags":[114,457859,334],"expertise":[],"class_list":["post-2148","article","type-article","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-biotech","category-innovation-en","category-pharma","tag-innovation-en","tag-machine-learning-en-en","tag-pharmaceutical"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.1 (Yoast SEO v28.1) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Artificial Intelligence Drug Discovery: Innovation in Pharmacy - Leyton Canada<\/title>\n<meta name=\"description\" content=\"Explore the future of pharmaceutical R&amp;D with AI and discover how Artificial Intelligence is transforming drug discovery.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/leyton.com\/ca\/en\/insights\/articles\/artificial-intelligence-drug-discovery-innovation-in-pharmacy\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Artificial Intelligence Drug Discovery: Innovation in Pharmacy\" \/>\n<meta property=\"og:description\" content=\"Explore the future of pharmaceutical R&amp;D with AI and discover how Artificial Intelligence is transforming drug discovery.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/leyton.com\/ca\/en\/insights\/articles\/artificial-intelligence-drug-discovery-innovation-in-pharmacy\/\" \/>\n<meta property=\"og:site_name\" content=\"Leyton Canada\" \/>\n<meta property=\"article:modified_time\" content=\"2026-07-26T15:03:09+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/leyton.com\/wp-content\/blogs.dir\/3\/files\/2025\/05\/AI-in-drug-discorvery-pharmaceutical-field-1-1-1024x349.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"349\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/leyton.com\\\/ca\\\/en\\\/insights\\\/articles\\\/artificial-intelligence-drug-discovery-innovation-in-pharmacy\\\/\",\"url\":\"https:\\\/\\\/leyton.com\\\/ca\\\/en\\\/insights\\\/articles\\\/artificial-intelligence-drug-discovery-innovation-in-pharmacy\\\/\",\"name\":\"Artificial Intelligence Drug Discovery: Innovation in Pharmacy - 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