Condition aware learning enables robust prediction of oligonucleotide melting behavior across diverse chemistries and assay conditions
Oligonucleotide melting temperature is a fundamental determinant of nucleic acid hybridization and underpins the design of molecular diagnostics, polymerase chain reaction assays, and many other biotechnology applications. However, accurately predicting melting behavior remains difficult because it depends not only on sequence composition, but also on experimental conditions and chemical modifications commonly used in modern assay design. Existing thermodynamic models rely on fixed parameterizations that are often difficult to extend across diverse reaction environments and nucleotide chemistries. Here we show that a condition-aware nucleotide language model can accurately predict oligonucleotide melting behavior across diverse experimental conditions and both unmodified and chemically modified oligonucleotides. By combining contextual sequence representations with explicit information describing the reaction environment, the framework achieves sub-degree prediction accuracy and reduces prediction error for locked nucleic acid-modified oligonucleotides by up to 25% relative to nearest-neighbor thermodynamic approaches. The model also more accurately captures the thermal effects introduced by nucleotide modification and maintains strong performance on independent benchmark datasets spanning experimental conditions substantially different from those represented during training. Our results demonstrate that learned sequence representations can complement classical thermodynamic models by capturing context-dependent effects that are difficult to encode using fixed parameter tables alone. More broadly, this work provides a scalable framework for predicting oligonucleotide melting behavior across diverse chemistries and assay conditions, supporting more reliable molecular assay design.