diff --git a/ginkgo_ai_client/queries.py b/ginkgo_ai_client/queries.py index 94323f0..1ba5085 100644 --- a/ginkgo_ai_client/queries.py +++ b/ginkgo_ai_client/queries.py @@ -464,12 +464,43 @@ class MultimodalDiffusionMaskedResponse(ResponseBase): class RNADiffusionMaskedQuery(QueryBase): """A query to perform masked sampling using a mRNA diffusion model. + Parameters + ---------- + three_utr: str + The three UTR sequence, of the form "ATTGTAC..." + five_utr: str + The five UTR sequence, of the form "ATTGTAC..." + protein_sequence: str + The protein sequence, of the form "MLKKRRK...LP-" (the last character denotes a + stop codon). + species: str + The species, e.g. "HOMO_SAPIENS" + temperature: float, optional (default=1.0) + Sampling temperature, a value between 0 and 1. + decoding_order_strategy: str, optional (default="entropy") + Strategy for decoding order, must be either "max_prob" or "entropy". + unmaskings_per_step: int, optional (default=4) + Number of tokens to unmask per step + num_samples: int, optional (default=1) + Number of samples to generate + model: str + The model to use for the inference. + query_name: Optional[str] = None + The name of the query. It will appear in the API response and can be used to handle exceptions. + + Returns + ------- + MultimodalDiffusionMaskedResponse + ``client.send_request(query)`` returns a ``MultimodalDiffusionMaskedResponse`` with + attributes ``samples`` (a list of predicted samples, with modality name: predicted sequence) + and ``query_name`` (the original query's name). + Examples -------- >>> query = RNADiffusionMaskedQuery( ... three_utr="ATTGTAC", ... five_utr="ATTGTAC", - ... protein_sequence="ATTGTAC", + ... protein_sequence="MLKKRRK", ... species="HOMO_SAPIENS", ... model="mrna-foundation", ... temperature=1.0, @@ -494,7 +525,6 @@ class RNADiffusionMaskedQuery(QueryBase): def to_request_params(self) -> Dict: data = { - # Many people in the field use [MASK] but our API client uses for all models "three_utr": self.three_utr, "five_utr": self.five_utr, "sequence_aa": self.protein_sequence,