DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence (AI) in Drug Discovery Market by Component (Software, Service), Technology (ML, DL), Application (Neurodegenerative Diseases, Immuno-Oncology, CVD), End User (Pharmaceutical & Biotechnology, CRO), Region - Global forecast to 2024" report has been added to ResearchAndMarkets.com's offering.

The Artificial intelligence/AI in drug discovery Market is projected to reach USD 4.0 billion by 2027 from USD 0.6 billion in 2022, at a CAGR of 45.7% during the forecast period. The growth of this market is primarily driven by factors such as the need to control drug discovery & development costs and reduce the overall time taken in this process, the rising adoption of cloud-based applications and services. On the other hand, the inadequate availability of skilled labor is key factor restraining the market growth at 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

On the basis of offering, the AI in drug discovery market is bifurcated into software and services. the services 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. The advantages and benefits associated with these services and the strong demand for AI services among end users are the key factors for the growth of this segment.

Machine learning technology segment accounted for the largest share of the global AI in drug discovery market

On the basis of technology, the AI in drug discovery market is segmented into machine learning and other technologies. The machine learning segment accounted for the largest share of the global market in 2021 and expected to grow at the highest CAGR during the forecast period. High adoption of machine learning technology among CRO, pharmaceutical and biotechnology companies and capability of these technologies to extract insights from data sets, which helps accelerate the drug discovery process are some of the factors supporting the market growth of this segment.

Pharmaceutical & biotechnology companies segment expected to hold the largest share of the market in 2022

On the basis of end user, the AI in drug discovery market is divided into pharmaceutical & biotechnology companies, CROs, and research centers and academic & government institutes. In 2021, the pharmaceutical & biotechnology companies segment accounted for the largest share of the AI in drug discovery market. On the other hand, research centers and academic & government institutes are expected to witness the highest CAGR during the forecast period. The strong demand for AI-based tools in making the entire drug discovery process more time and cost-efficient is the key growth factor of pharmaceutical and biotechnology end-user segment.

Key Topics Covered:

1 Introduction

2 Research Methodology

3 Executive Summary

4 Premium Insights

4.1 Growing Need to Control Drug Discovery & Development Costs is a Key Factor Driving the Adoption of AI in Drug Discovery Solutions

4.2 Services Segment to Witness the Highest Growth During the Forecast Period

4.3 Deep Learning Segment Accounted for the Largest Market Share in 2021

4.4 North America is the Fastest-Growing Regional Market for AI in Drug Discovery

5 Market Overview

5.1 Introduction

5.2 Market Dynamics

5.2.1 Market Drivers

5.2.1.1 Growing Number of Cross-Industry Collaborations and Partnerships

5.2.1.2 Growing Need to Control Drug Discovery & Development Costs and Reduce Time Involved in Drug Development

5.2.1.3 Patent Expiry of Several Drugs

5.2.2 Market Restraints

5.2.2.1 Shortage of AI Workforce and Ambiguous Regulatory Guidelines for Medical Software

5.2.3 Market Opportunities

5.2.3.1 Growing Biotechnology Industry

5.2.3.2 Emerging Markets

5.2.3.3 Focus on Developing Human-Aware AI Systems

5.2.3.4 Growth in the Drugs and Biologics Market Despite the COVID-19 Pandemic

5.2.4 Market Challenges

5.2.4.1 Limited Availability of Data Sets

5.3 Value Chain Analysis

5.4 Porter's Five Forces Analysiss

5.5 Ecosystem

5.6 Technology Analysis

5.7 Pricing Analysis

5.8 Business Models

5.9 Regulations

5.10 Conferences and Webinars

5.11 Case Study Analysis

6 Artificial Intelligence in Drug Discovery Market, by Offering

7 Artificial Intelligence in Drug Discovery Market, by Technology

8 Artificial Intelligence in Drug Discovery Market, by Application

9 Artificial Intelligence in Drug Discovery Market, by End-user

10 Artificial Intelligence in Drug Discovery Market, by Region

11 Competitive Landscape

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/q5pvns

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Worldwide Artificial Intelligence (AI) in Drug Discovery Market to reach $ 4.0 billion by 2027 at a CAGR of 45.7% - ResearchAndMarkets.com - Business...

CAMBRIDGE, Mass., June 30, 2022 /PRNewswire/ -- VistaPath, the leading provider of artificial intelligence (AI)-based, data-driven pathology processing platforms, today announced that it has secured $4 million in seed funding led by Moxxie Ventures with participation from NextGen Venture Partners and First Star Ventures. With this latest round, VistaPath will further advance its mission to modernize pathology labs, delivering faster, more accurate diagnoses that lead to optimal patient care.

"We're excited to be working with investors who share our desire to impact the lives and clinical outcomes of patients. This funding will support full-scale development and delivery of our innovative products, as well as the expansion of our operational and technical capabilitiesallowing us to better serve the clinical and life science markets," says Timothy Spong, CEO of VistaPath.

VistaPath's Sentinel is a first-of-its-kind pathology processing platform designed to seamlessly deliver a range of solutions for critical lab processes. The company's first application, released in 2021, is a tissue grossing platform that automates the process of receiving, assessing, and processing tissue samples. The platform uses a high-quality video system combined with AI to assess specimens and create a gross report 93% faster than human technicians with 43% more accuracy. Additional applications are slated to be released later this year.

"Pathology is the study of disease and connects every aspect of patient care. We believe that advances in computer vision and AI can bring great improvements to the pathology industry and ultimately lead to better outcomes for patients. We believe the team at VistaPath is building a best-in-class product for pathology labs and are proud to lead this investment round", says Alex Roetter, General Partner at Moxxie Ventures.

About VistaPath

VistaPath is modernizing pathology labs using computer vision and artificial intelligence. They provide clients with significant quality, workflow, and strategic benefits with the overall goal of delivering improved results for pathologists, clinicians, and patients. The Sentinel is the company's first product. Learn more at vistapathbio.com.

About Moxxie Ventures

Moxxie Ventures is an early stage venture firm focused on backing exceptional founders who make life and work better. Moxxie is based in San Francisco, CA and Boulder, CO. Learn more at moxxie.vc.

SOURCE VistaPath

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VistaPath Raises $4M to Modernize Pathology Labs Using Computer Vision and Artificial Intelligence - PR Newswire

Explanation methods that help users understand and trust machine-learning models often describe how much certain features used in the model contribute to its prediction. For example, if a model predicts a patients risk of developing cardiac disease, a physician might want to know how strongly the patients heart rate data influences that prediction.

But if those features are so complex or convoluted that the user cant understand them, does the explanation method do any good?

MIT researchers are striving to improve the interpretability of features so decision makers will be more comfortable using the outputs of machine-learning models. Drawing on years of field work, they developed a taxonomy to help developers craft features that will be easier for their target audience to understand.

We found that out in the real world, even though we were using state-of-the-art ways of explaining machine-learning models, there is still a lot of confusion stemming from the features, not from the model itself, says Alexandra Zytek, an electrical engineering and computer science PhD student and lead author of a paper introducing the taxonomy.

To build the taxonomy, the researchers defined properties that make features interpretable for five types of users, from artificial intelligence experts to the people affected by a machine-learning models prediction. They also offer instructions for how model creators can transform features into formats that will be easier for a layperson to comprehend.

They hope their work will inspire model builders to consider using interpretable features from the beginning of the development process, rather than trying to work backward and focus on explainability after the fact.

MIT co-authors include Dongyu Liu, a postdoc; visiting professor Laure Berti-quille, research director at IRD; and senior author Kalyan Veeramachaneni, principal research scientist in the Laboratory for Information and Decision Systems (LIDS) and leader of the Data to AI group. They are joined by Ignacio Arnaldo, a principal data scientist at Corelight. The research is published in the June edition of the Association for Computing Machinery Special Interest Group on Knowledge Discovery and Data Minings peer-reviewed Explorations Newsletter.

Real-world lessons

Features are input variables that are fed to machine-learning models; they are usually drawn from the columns in a dataset. Data scientists typically select and handcraft features for the model, and they mainly focus on ensuring features are developed to improve model accuracy, not on whether a decision-maker can understand them, Veeramachaneni explains.

For several years, he and his team have worked with decision makers to identify machine-learning usability challenges. These domain experts, most of whom lack machine-learning knowledge, often dont trust models because they dont understand the features that influence predictions.

For one project, they partnered with clinicians in a hospital ICU who used machine learning to predict the risk a patient will face complications after cardiac surgery. Some features were presented as aggregated values, like the trend of a patients heart rate over time. While features coded this way were model ready (the model could process the data), clinicians didnt understand how they were computed. They would rather see how these aggregated features relate to original values, so they could identify anomalies in a patients heart rate, Liu says.

By contrast, a group of learning scientists preferred features that were aggregated. Instead of having a feature like number of posts a student made on discussion forums they would rather have related features grouped together and labeled with terms they understood, like participation.

With interpretability, one size doesnt fit all. When you go from area to area, there are different needs. And interpretability itself has many levels, Veeramachaneni says.

The idea that one size doesnt fit all is key to the researchers taxonomy. They define properties that can make features more or less interpretable for different decision makers and outline which properties are likely most important to specific users.

For instance, machine-learning developers might focus on having features that are compatible with the model and predictive, meaning they are expected to improve the models performance.

On the other hand, decision makers with no machine-learning experience might be better served by features that are human-worded, meaning they are described in a way that is natural for users, and understandable, meaning they refer to real-world metrics users can reason about.

The taxonomy says, if you are making interpretable features, to what level are they interpretable? You may not need all levels, depending on the type of domain experts you are working with, Zytek says.

Putting interpretability first

The researchers also outline feature engineering techniques a developer can employ to make features more interpretable for a specific audience.

Feature engineering is a process in which data scientists transform data into a format machine-learning models can process, using techniques like aggregating data or normalizing values. Most models also cant process categorical data unless they are converted to a numerical code. These transformations are often nearly impossible for laypeople to unpack.

Creating interpretable features might involve undoing some of that encoding, Zytek says. For instance, a common feature engineering technique organizes spans of data so they all contain the same number of years. To make these features more interpretable, one could group age ranges using human terms, like infant, toddler, child, and teen. Or rather than using a transformed feature like average pulse rate, an interpretable feature might simply be the actual pulse rate data, Liu adds.

In a lot of domains, the tradeoff between interpretable features and model accuracy is actually very small. When we were working with child welfare screeners, for example, we retrained the model using only features that met our definitions for interpretability, and the performance decrease was almost negligible, Zytek says.

Building off this work, the researchers are developing a system that enables a model developer to handle complicated feature transformations in a more efficient manner, to create human-centered explanations for machine-learning models. This new system will also convert algorithms designed to explain model-ready datasets into formats that can be understood by decision makers.

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Building explainability into the components of machine-learning models - MIT News

During its ambitious technological journey, the ship, which launched from Plymouth, England, in April, collected data and information to help researchers better understand issues affecting marine wildlife and ocean health, including acidification, microplastics, and global warming, according to project details.

MAS represents a significant step in fulfilling Promares mission to promote marine research and exploration throughout the world, Ayse Atauz Phaneuf, Promares president, said in a statement. This pioneering mission is the result of years of work and a global collaboration between Promare, IBM, and dozens of partners from across industries and academia.

Promare, IBM, and their partners have been chronicling MAS400s voyage through social media updates and a collection of livestream cameras that provide a first-hand account of what it encounters at sea like the time a school of dolphin swam alongside it.

People can also explore whats happening on deck by using a mission control dashboard on the projects website.

According to IBM, there are 6 AI-powered cameras, more than 30 sensors, and 15 Edge devices on the MAS400, which input into actionable recommendations for the AI Captain to interpret and analyze.

The technology makes it possible for the ship to adhere to maritime law while making crucial split-second decisions, like rerouting itself around hazards or marine animals, all without human interaction or intervention, the company said.

The ship is propelled and powered by magnetic electric propulsion motors, batteries, and solar panels on its exterior. It has a backup diesel engine.

While the project has set the stage for future unmanned journeys across the ocean, the ship did encounter some hiccups, researchers said.

The vessel had to make at least two pit stops to deal with technical interruptions, including a problem with its generator and the charging circuit for the generator starter batteries.

The problems prompted diversions to both the Azores and Nova Scotia in May.

Still, the teams behind the voyage took the setbacks in stride.

From the outset our goal was to attempt to cross the Atlantic autonomously, all the while collecting vital information about our ocean and climate, said Brett Phaneuf, who co-created the vessel. Success is not in the completed crossing, but in the team that made it happen and the knowledge we now possess and will share so that more and more ships like MAS can safely roam our seas and teach us more about the planet on which we live.

The 10,000 pound vessel left Nova Scotia on June 27 to complete its voyage. Its expected to arrive in Plymouth Harbor around noon Thursday, where it will be greeted by excited researchers.

A welcome ceremony will be held at 3 p.m., as MAS400 docks next to its namesake, the Mayflower II, a replica of the original ship that brought the Pilgrims to America in 1620.

Throughout the centuries, iconic ships have made their mark in maritime technology and discovery through journeys often thought impossible, Whit Perry, captain of the Mayflower II, said in a statement. How exciting to see history being made again on these shores with this extraordinary vessel.

Steve Annear can be reached at steve.annear@globe.com. Follow him on Twitter @steveannear.

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A new Mayflower that uses artificial intelligence has crossed the Atlantic and is set to dock in Plymouth - The Boston Globe