🔗 Label Prediction🔗 Unconstrained Generation🔗 Conditional Generation🔗 Optimization Generation

Conotoxin Optimization Generation

Input

✅Conotoxin: a conotoxin that needs to be optimized. For example, GCCSDPRCAWRC.

✅Positions: the positions that need to be optimized, replaced by X. For example, GCCXXXXCAWRC.

✅Subtype: subtype of action. For example, α7.

✅Potency: required potency. For example, High.

✅τ: temperature factor controls the diversity of conotoxins generated. The higher the value, the higher the diversity.

✅Number of generations: if it is not completed within 1200 seconds, it will automatically stop.

✅Model: model parameters trained at different stages of data augmentation. Please refer to the paper for details.

✅Seed: enter an integer as the random seed to ensure reproducible results. The default is random.