AINN-P1: A Compact Sequence-Only Protein Language Model Achieves Competitive Fitness Prediction on ProteinGym

Protein language models (PLMs) are increasingly central to protein engineering and drug discovery. Many high-performing systems, however, rely on large parameter counts, multiple sequence alignments (MSAs), explicit structural inputs, or computationally intensive attention mechanisms, limiting their accessibility and throughput. Here we present AINN-P1, a 167M-parameter protein language model trained exclusively on raw UniRef amino-acid sequences using an autoregressive next-token prediction objective. AINN-P1 employs a multiplicative LSTM (mLSTM) architecture--an attention-free, recurrent design that scales linearly with sequence length and avoids growing key-value caches during inference. We evaluate AINN-P1 on ProteinGym fitness prediction tasks spanning activity, binding, expression, and stability using a frozen-encoder protocol with lightweight few-shot regression heads. Under this protocol, AINN-P1 achieves an average Spearman{rho} of 0.441 across four task categories and a Spearman{rho} of 0.625 on stability--the highest among sequence-only models in our comparison set. Because our evaluation uses few-shot supervised regression rather than the zero-shot scoring employed by most ProteinGym leaderboard baselines, direct numerical comparison requires caution; we discuss this methodological distinction throughout. Beyond benchmark performance, AINN-P1 emphasizes practical deployability: its recurrent architecture avoids quadratic memory scaling, supports fixed-state inference on long sequences, and enables rapid adaptation through frozen embeddings rather than costly end-to-end fine-tuning. We discuss when sequence-only models are sufficient when structural information remains beneficial and how compact foundation models can serve as efficient front-end filters in drug discovery workflows.