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Artificial Intelligence in Drug Discovery Market to Hit USD 6.89 Billion by 2029 with 29.9% CAGR | MarketsandMarkets™ [Yahoo! Finance]

NVIDIA Corporation (NVDA)  More Company Research Source: Yahoo! Finance
Last nvidia corporation earnings: 2/13 04:20 pm Check Earnings Report
US:NASDAQ Investor Relations: investor.nvidia.com/home/default.aspx
USD 1.86 billion in 2024, at a CAGR of 29.9% from 2024 to 2029. The rising shift towards integrating AI for understanding diseases and small molecule design and optimization use cases augments the growth of the market. AI tools play a major role in accelerating target identification, optimizing lead compound selection, and predicting drug efficacy and toxicity. Supervised methods such as regression, decision trees, and neural networks help predict material properties and drug candidate profiles. In contrast, unsupervised techniques such as clustering algorithms (k-means and hierarchical clustering) and dimensionality reduction identify hidden trends, patterns, and groupings in data, aiding in novel drug discovery. Deep Learning is employed to predict molecular properties, design new compounds, and other applications. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are more commonly used for handling complex data, such as sequences or molecular structures. Gen [Read more]
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Stockreport

Artificial Intelligence in Drug Discovery Market to Hit USD 6.89 Billion by 2029 with 29.9% CAGR | MarketsandMarkets™ [Yahoo! Finance]

NVIDIA Corporation  (NVDA) 
More Company Research Source: Yahoo! Finance
Last nvidia corporation earnings: 2/13 04:20 pm Check Earnings Report
US:NASDAQ Investor Relations: investor.nvidia.com/home/default.aspx
USD 1.86 billion in 2024, at a CAGR of 29.9% from 2024 to 2029. The rising shift towards integrating AI for understanding diseases and small molecule design and optimization use cases augments the growth of the market. AI tools play a major role in accelerating target identification, optimizing lead compound selection, and predicting drug efficacy and toxicity. Supervised methods such as regression, decision trees, and neural networks help predict material properties and drug candidate profiles. In contrast, unsupervised techniques such as clustering algorithms (k-means and hierarchical clustering) and dimensionality reduction identify hidden trends, patterns, and groupings in data, aiding in novel drug discovery. Deep Learning is employed to predict molecular properties, design new compounds, and other applications. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are more commonly used for handling complex data, such as sequences or molecular structures. Gen [Read more]
IMPACT SNAPSHOT
EVENT TIME:
NVDA
Last Price
Price Change
Price Change %
Volume Shares
Max Up
Max Down
Volume Ratio
%
Don't Miss Out On The Next BIG Stock Move
Performance comparison Updated
EVENT DAY
Stock performance from the time of news release until the following 4pm ET market close
LAST PRICE AT
NEWS EVENT
SINCE EVENT
Multi-day stock performance from the time of the news release
LAST PRICE
VWAP
High:
MAX UP
High:
Low:
MAX DOWN
Low:
%
 POST NEWS RANGE
%
PRICE CHANGE
PRICE CHANGE PERCENTAGE
S&P 500 (SPX)
%
 VOLUME RATIO
%
VOLUME (SHARES)
TICKS
AVG SHARES PER TRADE
EVENT DAY
Event Day Chart will not be displayed beyond 90-day period of the event
PERFORMANCE SINCE EVENT
TIME AND VELOCITY ANALYSIS