Predicting drug-target binding affinity (DTA) plays a pivotal role in drug discovery and development. Although experimental assays can provide accurate binding affinity, they suffer from
high monetary costs and extensive time consumption for large-scale screens. To relieve this bottleneck, the DTA prediction with computational methods approach has attracted considerable attention in recent years due to its convenience and low cost. Advances in machine learning and artificial intelligence have enabled more accurate and reliable predictions of DTA, making them a valuable tool for drug discovery and development. Polish the paragraph above to make it more formal, logical, and academic paper style. Provide your response in exactly 6 paragraphs (separated nearby paragraphs with 3 aterisk symbols ***).
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