Fast differentiable DNA and protein sequence optimization for molecular design

Understand

Designing DNA and protein sequences with improved function has the potential to greatly accelerate synthetic biology.

• Machine learning models that accurately predict biological fitness from sequence are becoming a powerful tool for molecular design.
• Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are first approximated by a continuous representation which is then iteratively optimized with respect to the predictor oracle by gradient ascent.
• While elegant, this method suffers from vanishing gradients and may cause predictor pathologies leading to poor convergence.