In silico tools, particularly AI-driven predictive models, can significantly improve drug manufacturability by identifying potential issues early in the development process. BigHat Biosciences demonstrates that by augmenting physical experiments with these computational models, developers can explore a wider range of mutations and optimize antibody sequences for higher yields and improved biophysical properties. This approach derisks the manufacturing process by reducing the need for extensive lab testing and potentially salvaging suboptimal candidates through iterative optimization.
Read the full article at Genetic Engineering News
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Ali NematiWritten by Ali
