AI Article Synopsis
- The life sciences are transitioning from model-driven methods to data-driven approaches due to the explosive growth of data, necessitating more efficient processing technologies.
- Massively parallel accelerators like GPUs are crucial for developing bioinformatics methods, enabling faster data processing and analysis.
- Advancements in computational methods, including AI and future quantum computing, are expected to significantly impact the life sciences and drug discovery, highlighting three key waves of technological acceleration: GPU computing, AI, and quantum computing.
Article Abstract
The enormous growth in the amount of data generated by the life sciences is continuously shifting the field from model-driven science towards data-driven science. The need for efficient processing has led to the adoption of massively parallel accelerators such as graphics processing units (GPUs). Consequently, the development of bioinformatics methods nowadays often heavily depends on the effective use of these powerful technologies. Furthermore, progress in computational techniques and architectures continues to be highly dynamic, involving novel deep neural network models and artificial intelligence (AI) accelerators, and potentially quantum processing units in the future. These are expected to be disruptive for the life sciences as a whole and for drug discovery in particular. Here, we identify three waves of acceleration and their applications in a bioinformatics context: (i) GPU computing, (ii) AI and (iii) next-generation quantum computers.
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| http://dx.doi.org/10.1016/j.drudis.2024.103990 | DOI Listing |
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