Analysis of bias in binding classification models have been always focused on QSAR models, but how could a↵ect the inclusion of proteins to bias in QMSPR models remains unknown. Proteins are macromolecules constituted by amino acid residues covalently attached to one another, forming long linear sequences which identify them, defining its folding and its activity. The main value of DL in this context is that DL can directly learn from the sequence, capturing nonlinear dependencies and interaction e↵ects, and hence providing additional understanding about the structure of the biological data. The appropriate DL architecture to manage this kind of data are bi-directional Recurrent Neural Networks (RNN), well suited for modeling data with a sequential but non-causal structure, variable length and long-range dependencies. Baldi et al have applied bi-directional RNN to protein sequence for predicting secondary structure, for matching protein beta-sheet partners or for predicting residue-residue contact. However, classical RNN cannot hold very long-range dependencies and to overcome this issue Hochreiter et al applied Long Short Term Memory (LSTM) networks to classify amino acid sequences into superfamilies. Jurtz et al applied bi-directional LSTM to amino acid sequence for subcellular localization, secondary structure prediction and peptides binding to a major histocompatibility complex. In this paper, we analyse and quantify the e↵ect of di↵erent cross-validation strategies on the performance of binding prediction DL-based proteochemometrics models. Additionally, we compare these DL models with baseline logistic regression (LR) models and explore di↵erent representations for molecules.