Pretrained Transformer Encoder Parameters This component provides the pretrained parameters for a transformer encoder designed to extract feature representations from SMILES strings. The model was trained using masked token prediction to capture intricate patterns and long-range dependencies within molecular sequences. The transformer architecture includes: 10 sequential transformer blocks, Multi-head self-attention for contextualized token embeddings, Position-wise feed-forward layers with Gaussian Error Linear Unit (GELU) activation, residual connections, and layer normalization. The global molecular representation is derived from the start token embedding, which aggregates sequence-wide information during self-attention computations. Pretraining Dataset This component provides the dataset used to pretrain the transformer encoder. It integrates SMILES strings from the following sources: ChEMBL 33: ~2.4 million bioactive molecules with drug-like properties, GuacaMol v1: ~1.6 million molecules derived from ChEMBL 24, MOSES: ~1.8 million molecules selected from ZINC 15 for diversity and medicinal chemistry suitability, BindingDB: ~1.2 million unique small molecules bound to proteins, PDBbind v2020: ~15,710 unique small molecules bound to proteins. This model has been optimized for drug discovery applications, including protein-ligand binding affinity prediction, and can serve as a foundational tool for researchers working on cheminformatics, computational biology, and medicinal chemistry.