, /PRNewswire/ — The global artificial intelligence (AI) in drug discovery market is projected to reach USD 6.89 billion by 2029 from USD 1.86 billionin 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. Generative Adversarial Networks (GANs) …
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