Abstract
Summary
The artificial intelligence (AI) in drug discovery market is projected to reach USD 4.9 billion by 2028 from USD 0.9 billion in 2023, at a CAGR of 40.2% during the forecast period. AI expedites the identification and validation of potential drug targets by analyzing intricate biological data. This accelerates the selection of biologically relevant targets for therapeutic interventions. AI techniques such as machine learning enable rapid analysis and decision-making, reducing the time and resources required for drug discovery processes. This efficiency gains a competitive edge in the fast-paced pharmaceutical landscape. Therefore, aforementioned factors will drive the growth of this market. On the other hand, the inadequate availability of skilled labor is key factor restraining the market growth to a certain extent over the forecast period.
“Services segment is estimated to hold the major share in 2022 and also expected to grow at the highest over the forecast period.”
Based on offering, the AI in drug discovery market is bifurcated into software and services. The2022 and segment expected to account for the largest market share of the global AI in drug discovery services market in 2022 and expected to grow fastest CAGR during the forecast period. Access to AI technology and expertise through services reduces the barriers for pharmaceutical companies to adopt AI in drug discovery. This is particularly beneficial for smaller companies without extensive in-house AI capabilities, enabling them to harness the power of AI without significant upfront investments.
“Machine learning technology segment accounted for the largest share of the global AI in drug discovery market.”
Based on technology, the AI in drug discovery market is segmented into machine learning, natural language processing (NLP), context aware processing, and other technologies. The machine learning segment accounted for the largest share of the global market in 2022 and expected to grow at the highest CAGR during the forecast period. Machine learning enables the creation of predictive models that anticipate the behavior of potential drug candidates within the human body. This aids in identifying compounds with the highest likelihood of success, reducing the costs and time associated with unsuccessful candidates. Machine learning contributes to the development of personalized treatment strategies by analyzing patient data to predict individual responses to drugs. This facilitates tailoring treatments based on genetic, molecular, and clinical information, leading to more effective outcomes, which helps accelerate the drug discovery process are some of the factors supporting the market growth of this segment.
“Small Molecule Design and Optimization segment expected to hold the largest share of use case segment of the market in 2022.”
Based on use cases, the AI in drug discovery market is divided into small molecule design and optimization, understanding disease, safety and toxicity, vaccine design and optimization, antibody and other biologics design and optimization. In 2022, the small molecule design and optimization segment accounted for the largest share of the AI in drug discovery market. AI is employed in small molecule design and optimization for two main purposes. Firstly, it aids in identifying hit-like or lead-like compounds by screening existing chemical libraries or through generative de novo design. Secondly, AI optimizes the identified hits, ensuring favorable properties like binding affinity, toxicity, and synthesis, ultimately leading to the development of more effective and safer drug candidates. These factors contribute to the development and refinement of AI algorithms tailored for drug discovery use cases.
“North America to dominate the AI in drug discovery market in 2022”
The global AI in the drug discovery market is segmented into four major regions, namely, North America, Europe, APAC, and the Rest of the World. In 2022, North America accounted for the largest and the fastest-growing regional market for AI in drug discovery. North America hosts numerous pharmaceutical giants and biotechnology innovators that are actively exploring AI's capabilities in drug discovery. These industry leaders are investing significantly in AI-driven research and development, driving market growth. North America's well-established regulatory framework for pharmaceuticals and healthcare facilitates the integration of AI technologies while ensuring compliance with industry standards and guidelines. The above-mentioned factors will drive the market of AI in drug discovery in North America.
Breakdown of supply-side, demand side, primary interviews, by company type, designation, and region:
• By Company Type:
• By Supply Side: Tier 1 (31%), Tier 2 (28%), and Tier 3 (41%)
• By Demand Side: Purchase Managers (45%), Head of Artificial Intelligence, Machine Learning, Drug Discovery, and Computational Molecular Design (30%) and Research Scientists (25%).
• By Designation: C-level (31%), Director-level (25%), and Others (44%)
• By Region: North America (45%), Europe (20%), Asia Pacific (28%), South America (4%) and Middle East & Africa (3%).
The prominent players in this market are NVIDIA Corporation (US), Exscientia (UK), BenevolentAI (UK), Recursion (US), Insilico Medicine (US), Schrödinger, Inc. (US), Microsoft Corporation (US), Google (US), Atomwise Inc. (US), Illumina, Inc. (US), NuMedii, Inc. (US), XtalPi Inc. (US), Iktos (France), Tempus Labs (US), Deep Genomics, Inc. (Canada), Verge Genomics (US), BenchSci (Canada), Insitro (US), Valo Health (US), BPGbio, Inc. (US), IQVIA Inc (US), Labcorp (US), Tencent Holdings Limited (China), Predictive Oncology, Inc. (US), Celsius Therapeutics (US), CytoReason (Israel), Owkin, Inc. (US), Cloud Pharmaceuticals (US), Evaxion Biotech (Denmark), Standigm (South Korea), BIOAGE (US), Envisagenics (US), and Aria Pharmaceuticals, Inc. (US). Players adopted organic as well as inorganic growth strategies such as product launches and enhancements, and investments, partnerships, collaborations, joint ventures, funding, acquisition, expansions, agreements, sales contracts, and alliances to increase their offerings, cater to the unmet needs of customers, increase their profitability, and expand their presence in the global market.
Research Coverage
• The report studies the AI in drug discovery market based on offering, technology, therapeutic area, use case, process, end user, and region.
• The report analyzes factors (such as drivers, restraints, opportunities, and challenges) affecting the market growth.
• The report evaluates the opportunities and challenges in the market for stakeholders and provides details of the competitive landscape for market leaders.
• The report studies micro-markets with respect to their growth trends, prospects, and contributions to the total AI in drug discovery market.
• The report forecasts the revenue of market segments with respect to five major regions.
Reasons to Buy the Report
The report can help established firms as well as new entrants/smaller firms to gauge the pulse of the market, which, in turn, would help them garner a greater share. Firms purchasing the report could use one or a combination of the below-mentioned five strategies.
This report provides insights into the following pointers:
Analysis of key drivers (growing number of cross-industry collaborations and partnerships, growing need to control drug discovery & development costs and reduce time involved in drug development, patent expiry of several drugs), restraints (shortage of AI workforce and ambiguous regulatory guidelines for medical software), opportunities (growing biotechnology industry, emerging markets, focus on developing human-aware AI systems, growth in the drugs and biologics market despite the COVID-19 pandemic), and challenges (limited availability of data sets) influencing the growth of AI in drug discovery market.
Product Development/Innovation: Detailed insights on upcoming technologies, research and development activities, and product launches in the AI in drug discovery market.
Market Development: Comprehensive information about lucrative emerging markets. The report analyzes the markets for various types of AI in drug discovery solutions across regions.
Market Diversification: Exhaustive information about products, untapped regions, recent developments, and investments in the AI in drug discovery market.
Competitive Assessment: In-depth assessment of market shares, strategies, products, distribution networks, and manufacturing capabilities of the leading players in the AI in drug discovery market.
Table of Contents
1 INTRODUCTION 38
1.1 STUDY OBJECTIVES 38
1.2 MARKET DEFINITION 38
1.2.1 INCLUSIONS AND EXCLUSIONS 39
1.3 MARKET SCOPE 40
1.3.1 MARKET SEGMENTATION 40
1.3.2 REGIONAL SCOPE 41
1.3.3 YEARS CONSIDERED 41
1.3.4 CURRENCY CONSIDERED 42
1.4 STAKEHOLDERS 42
1.5 SUMMARY OF CHANGES 43
1.5.1 RECESSION IMPACT 44
2 RESEARCH METHODOLOGY 45
2.1 RESEARCH DATA 45
2.2 SECONDARY SOURCES 46
2.2.1 KEY DATA FROM SECONDARY SOURCES 47
2.3 PRIMARY DATA 47
2.3.1 PRIMARY SOURCES 48
- 2.3.1.1 Key data from primary sources 49
- 2.3.1.2 Key industry insights 50
2.3.2 BREAKDOWN OF PRIMARY INTERVIEWS 50
2.4 MARKET SIZE ESTIMATION 51
2.5 MARKET BREAKDOWN AND DATA TRIANGULATION 58
2.6 ASSUMPTIONS 59
2.6.1 MARKET SIZING ASSUMPTIONS 59
2.6.2 OVERALL STUDY ASSUMPTIONS 59
2.7 LIMITATIONS 60
2.8 RISK ASSESSMENT 60
2.9 RECESSION IMPACT ANALYSIS 61
3 EXECUTIVE SUMMARY 63
4 PREMIUM INSIGHTS 68
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI IN DRUG DISCOVERY MARKET 68
4.2 AI IN DRUG DISCOVERY MARKET: REGIONAL LANDSCAPE 69
4.3 AI IN DRUG DISCOVERY MARKET: GEOGRAPHIC GROWTH OPPORTUNITIES 69
4.4 NORTH AMERICAN AI IN DRUG DISCOVERY MARKET, BY END USER & COUNTRY, 2022 70
4.5 AI IN DRUG DISCOVERY MARKET, BY OFFERING 70
4.6 AI IN DRUG DISCOVERY MARKET, BY TECHNOLOGY 71
4.7 AI IN DRUG DISCOVERY MARKET, BY THERAPEUTIC AREA 71
4.8 AI IN DRUG DISCOVERY MARKET, BY PROCESS 72
4.9 AI IN DRUG DISCOVERY MARKET, BY USE CASE 72
4.10 AI IN DRUG DISCOVERY MARKET, BY END USER 73
5 MARKET OVERVIEW 74
5.1 INTRODUCTION 74
5.2 MARKET DYNAMICS 74
5.2.1 DRIVERS 75
- 5.2.1.1 Growing cross-industry collaborations and partnerships 75
- 5.2.1.2 Growing need to reduce time and cost of drug discovery and development 77
- 5.2.1.3 Patent expiry of several drugs 77
5.2.2 RESTRAINTS 78
- 5.2.2.1 Shortage of AI workforce and ambiguous regulatory guidelines for medical software 78
5.2.3 OPPORTUNITIES 79
- 5.2.3.1 Growing biotechnology industry 79
- 5.2.3.2 Emerging markets 79
- 5.2.3.3 Focus on developing human-aware AI systems 80
5.2.4 CHALLENGES 80
- 5.2.4.1 Limited availability of data sets 80
- 5.2.4.2 Lack of required tools and usability 80
6 INDUSTRY INSIGHTS 82
6.1 OVERVIEW OF KEY INDUSTRY TRENDS 82
6.1.1 EVOLUTION OF AI IN DRUG DISCOVERY 82
6.1.2 COMPUTER-AIDED DRUG DESIGN AND AI 84
6.2 SUPPLY CHAIN ANALYSIS 84
6.3 PORTER’S FIVE FORCES ANALYSIS 85
6.3.1 INTENSITY OF COMPETITIVE RIVALRY 85
6.3.2 BARGAINING POWER OF BUYERS 85
6.3.3 BARGAINING POWER OF SUPPLIERS 86
6.3.4 THREAT OF SUBSTITUTES 86
6.3.5 THREAT OF NEW ENTRANTS 86
6.4 ECOSYSTEM/MARKET MAP 86
6.5 TECHNOLOGY ANALYSIS 87
6.5.1 DRY LAB SERVICES 87
6.5.2 WET LAB SERVICES 90
- 6.5.2.1 Chemistry services 90
- 6.5.2.2 BIOLOGICAL SERVICES 91
- 6.5.2.2.1 Single-cell analysis 92
6.6 PRICING ANALYSIS 95
6.6.1 AVERAGE SELLING PRICE TRENDS, BY REGION 95
6.6.2 INDICATIVE PRICING ANALYSIS, BY PROCESS 96
6.7 BUSINESS MODELS 96
6.8 CASE STUDY ANALYSIS 99
6.8.1 CASE STUDY 1: BRISTOL MYERS SQUIBB AND EXSCIENTIA 99
6.8.2 CASE STUDY 2: APEIRON LLC AND EXSCIENTIA 100
6.9 REGULATORY ANALYSIS 101
6.9.1 AI IN DRUG DISCOVERY MARKET: REGULATORY LANDSCAPE, BY REGION 101
6.9.2 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 103
6.10 PATENT ANALYSIS 104
6.11 KEY CONFERENCES & EVENTS (Q1 2023-Q2 2024) 108
6.12 TRENDS/DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESSES 109
6.13 KEY STAKEHOLDERS & BUYING CRITERIA 109
6.13.1 KEY STAKEHOLDERS IN BUYING PROCESS 109
6.13.2 BUYING CRITERIA FOR AI IN DRUG DISCOVERY MARKET 110
6.14 AI-DERIVED CLINICAL ASSETS 110
6.15 UNMET NEEDS 120
6.15.1 UNMET NEEDS IN AI IN DRUG DISCOVERY 120
6.15.2 SINGLE-CELL ANALYSIS LANDSCAPE: KEY CHALLENGES AND PAIN POINTS IN DRUG DISCOVERY 121
6.15.3 KEY UNMET NEEDS AND PAIN POINTS FOR AI APPLICATIONS IN SINGLE-CELL ANALYSIS FOR DRUG DISCOVERY 122
7 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY OFFERING 123
7.1 INTRODUCTION 124
7.2 SERVICES 124
7.2.1 SERVICES SEGMENT TO WITNESS HIGHEST GROWTH 124
7.3 SOFTWARE 125
7.3.1 BENEFITS OF SOFTWARE IN DRUG DISCOVERY AND STRONG DEMAND AMONG END USERS TO DRIVE GROWTH 125
8 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY TECHNOLOGY 127
8.1 INTRODUCTION 128
8.2 MACHINE LEARNING 128
8.2.1 DEEP LEARNING 130
- 8.2.1.1 Deep learning to see growing adoption in drug discovery 130
8.2.2 SUPERVISED LEARNING 131
- 8.2.2.1 Applications in drug repositioning to drive market 131
8.2.3 REINFORCEMENT LEARNING 132
- 8.2.3.1 Potential for machines and software to automatically determine behavior to support adoption 132
8.2.4 UNSUPERVISED LEARNING 133
- 8.2.4.1 Unpredictability to affect end-user adoption 133
8.2.5 OTHER MACHINE LEARNING TECHNOLOGIES 134
8.3 NATURAL LANGUAGE PROCESSING 135
8.3.1 POTENTIAL APPLICATIONS IN DATA IDENTIFICATION TO SUPPORT GROWTH 135
8.4 CONTEXT-AWARE PROCESSING & CONTEXT-AWARE COMPUTING 136
8.4.1 RISING PROCESSING POWER, IMPROVED CONNECTIVITY TO BOOST USAGE 136
8.5 OTHER TECHNOLOGIES 137
9 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET
9.1 INTRODUCTION 140
9.2 ONCOLOGY 140
9.2.1 HIGH PREVALENCE OF CANCER AND SHORTAGE OF EFFECTIVE CANCER DRUGS TO DRIVE SEGMENT GROWTH 140
9.3 INFECTIOUS DISEASES 141
9.3.1 RISING EPIDEMIC OUTBREAKS TO BOOST DRUG DISCOVERY ACTIVITY 141
9.4 NEUROLOGY 143
9.4.1 NEED TO BOOST DISCOVERY AND DEVELOPMENT TO DRIVE MARKET 143
9.5 CARDIOVASCULAR DISEASES 144
9.5.1 RISING DEMAND FOR CVD DRUGS TO DRIVE SEGMENT 144
9.6 IMMUNOLOGY 145
9.6.1 GROWING DRUG PIPELINE FOR IMMUNOLOGICAL DISORDERS TO FAVOR MARKET GROWTH 145
9.7 METABOLIC DISEASES 146
9.7.1 ROLE OF AI IN UNCOVERING SMALL-MOLECULE THERAPIES TO DRIVE ADOPTION 146
9.8 OTHER THERAPEUTIC AREAS 148
10 AI IN DRUG DISCOVERY MARKET, BY PROCESS 149
10.1 INTRODUCTION 150
10.2 TARGET IDENTIFICATION & SELECTION 151
10.2.1 DEVELOPMENT OF NEW TECHNOLOGIES TO SUPPORT MARKET GROWTH 151
10.3 TARGET VALIDATION 152
10.3.1 RISING EMPHASIS ON TARGET VALIDATION TO AVOID LATE-STAGE FAILURE 152
10.4 HIT IDENTIFICATION & PRIORITIZATION 154
10.4.1 NEED FOR LARGE-SCALE DATA ANALYSIS TO DRIVE ADOPTION 154
10.5 HIT-TO-LEAD IDENTIFICATION/LEAD GENERATION 155
10.5.1 HIT-TO-LEAD IDENTIFICATION TO HOLD LARGEST MARKET SHARE 155
10.6 LEAD OPTIMIZATION 156
10.6.1 NEED FOR TRANSPARENT PRESENTATION AND ANALYSIS TO BOOST FOCUS ON LEAD OPTIMIZATION 156
10.7 CANDIDATE SELECTION & VALIDATION 158
10.7.1 POSSIBILITY OF DRUG FAILURE DURING DEVELOPMENT TO DRIVE ADOPTION OF CANDIDATE VALIDATION SERVICES 158
11 AI IN DRUG DISCOVERY MARKET, BY USE CASE 160
11.1 INTRODUCTION 161
11.2 SMALL-MOLECULE DESIGN & OPTIMIZATION 162
11.2.1 AVAILABILITY OF WELL-VALIDATED AI TOOLS TO BOOST MARKET GROWTH 162
11.3 UNDERSTANDING DISEASE 164
11.3.1 RISING DATA MINING TO LINK TARGETS TO DISEASES AND DRUG REPURPOSING TO DRIVE MARKET 164
11.4 SAFETY & TOXICITY 165
11.4.1 TOXICOLOGY AND OFF-TARGET EFFECT PREDICTION, PK/PD SIMULATION, AND QSP MODELING TO PROPEL MARKET GROWTH 165
11.5 VACCINE DESIGN & OPTIMIZATION 167
11.5.1 GROWING ADOPTION OF AI FOR EPITOPE SELECTION, PREDICTION, AND BINDING TO AUGMENT MARKET GROWTH 167
11.6 ANTIBODY & OTHER BIOLOGIC DESIGN & OPTIMIZATION 169
11.6.1 GROWING ADOPTION OF AI FOR ANTIBODY PROPERTY PREDICTION TO DRIVE MARKET 169
12 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY END USER 171
12.1 INTRODUCTION 172
12.2 PHARMACEUTICAL & BIOTECHNOLOGY COMPANIES 172
12.2.1 RISING DEMAND FOR SOLUTIONS TO CUT TIME AND COSTS OF DRUG DEVELOPMENT TO DRIVE MARKET 172
12.3 CONTRACT RESEARCH ORGANIZATIONS 175
12.3.1 GROWING TREND OF OUTSOURCING TO PROVIDE SIGNIFICANT OPPORTUNITIES FOR CONTRACT RESEARCH ORGANIZATIONS 175
12.4 RESEARCH CENTERS AND ACADEMIC & GOVERNMENT INSTITUTES 177
12.4.1 SEGMENT TO REGISTER HIGHEST CAGR OVER FORECAST PERIOD 177
13 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY REGION 179
13.1 INTRODUCTION 180
13.2 NORTH AMERICA 180
13.2.1 NORTH AMERICA: RECESSION IMPACT 180
13.2.2 US 185
- 13.2.2.1 Strong economy and trend of early adoption of technologies to drive market 185
13.2.3 CANADA 190
- 13.2.3.1 Growing research on AI technologies and emergence of new AI-based start-ups to support market growth 190
13.2.4 MEXICO 194
- 13.2.4.1 Government initiatives to support market growth 194
13.3 EUROPE 197
13.3.1 EUROPE: RECESSION IMPACT 198
13.3.2 UK 202
- 13.3.2.1 UK to hold largest share in Europe 202
13.3.3 GERMANY 207
- 13.3.3.1 Government support and favorable training programs to propel market growth 207
13.3.4 FRANCE 211
- 13.3.4.1 Strong government support and favorable strategies & initiatives to drive market 211
13.3.5 ITALY 214
- 13.3.5.1 Strong pharmaceutical industry to support market growth 214
13.3.6 REST OF EUROPE 218
13.4 ASIA PACIFIC 222
13.4.1 ASIA PACIFIC: RECESSION IMPACT 222
13.4.2 JAPAN 226
- 13.4.2.1 Japan to dominate APAC market 226
13.4.3 CHINA 230
- 13.4.3.1 Growing CMO market and cross-industry collaborations to drive market 230
13.4.4 INDIA 234
- 13.4.4.1 Steady adoption of AI technologies to support market growth 234
13.4.5 REST OF ASIA PACIFIC 238
13.5 SOUTH AMERICA 242
13.5.1 SHORTAGE OF SKILLED LABOR AND GROWING REQUIREMENTS TO DRIVE AI ADOPTION 242
13.5.2 SOUTH AMERICA: RECESSION IMPACT 243
13.6 MIDDLE EAST & AFRICA 246
13.6.1 LIMITED COMPANY PRESENCE AND LARGE SCOPE FOR GROWTH TO FAVOR AI MARKET 246
13.6.2 MIDDLE EAST & AFRICA: RECESSION IMPACT 246
14 COMPETITIVE LANDSCAPE 251
14.1 KEY PLAYER STRATEGIES/RIGHT TO WIN 251
14.2 REVENUE ANALYSIS 256
14.3 MARKET SHARE ANALYSIS 257
14.4 COMPANY EVALUATION MATRIX 260
14.4.1 STARS 260
14.4.2 EMERGING LEADERS 260
14.4.3 PERVASIVE PLAYERS 260
14.4.4 PARTICIPANTS 261
14.4.5 COMPANY FOOTPRINT 262
14.5 START-UP/SME EVALUATION MATRIX 266
14.5.1 PROGRESSIVE COMPANIES 266
14.5.2 RESPONSIVE COMPANIES 266
14.5.3 DYNAMIC COMPANIES 266
14.5.4 STARTING BLOCKS 266
14.5.5 AI IN DRUG DISCOVERY MARKET: COMPETITIVE BENCHMARKING 268
14.6 COMPETITIVE SCENARIOS AND TRENDS 271
14.6.1 PRODUCT LAUNCHES & ENHANCEMENTS 271
14.6.2 DEALS 273
14.6.3 OTHER DEVELOPMENTS 274
15 COMPANY PROFILES 276
15.1 KEY PLAYERS 276
15.1.1 NVIDIA CORPORATION 276
15.1.2 EXSCIENTIA 284
15.1.3 GOOGLE 294
15.1.4 BENEVOLENTAI 299
15.1.5 RECURSION 303
15.1.6 INSILICO MEDICINE 307
15.1.7 SCHRÖDINGER, INC 314
15.1.8 MICROSOFT CORPORATION 319
15.1.9 ATOMWISE INC 323
15.1.10 ILLUMINA, INC 325
15.1.11 NUMEDII, INC 330
15.1.12 XTALPI INC 332
15.1.13 IKTOS 335
15.1.14 TEMPUS LABS 341
15.1.15 DEEP GENOMICS, INC 345
15.1.16 VERGE GENOMICS 347
15.1.17 BENCHSCI 349
15.1.18 INSITRO 351
15.1.19 VALO HEALTH 353
15.1.20 BPGBIO, INC 356
15.2 OTHER EMERGING PLAYERS 358
15.2.1 PREDICTIVE ONCOLOGY, INC 358
15.2.2 LABCORP 359
15.2.3 IQVIA INC 360
15.2.4 TENCENT HOLDINGS LIMITED 361
15.2.5 CELSIUS THERAPEUTICS 362
15.2.6 CYTOREASON 363
15.2.7 OWKIN, INC 364
15.2.8 CLOUD PHARMACEUTICALS 364
15.2.9 EVAXION BIOTECH 365
15.2.10 STANDIGM 365
15.2.11 BIOAGE LABS 366
15.2.12 ENVISAGENICS 366
15.2.13 ARIA PHARMACEUTICALS, INC 367
16 APPENDIX 368
16.1 DISCUSSION GUIDE 368
16.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 375
16.3 CUSTOMIZATION OPTIONS 377
16.4 RELATED REPORTS 378
16.5 AUTHOR DETAILS 379
2023 VS. 2028 (USD MILLION) 65