Following Schreck et al., machine learning models mimic the mathematical relationship between the molecules and their residual depths as inputs and the corresponding expected costs as output. To digitise molecules into mathematical models, extended-connectivity fingerprint (ECFP) was applied, which is a topological fingerprint to convert the circular structure of neighborhood of each non-hydrogen atom into bytes. In this work, the radius of the fingerprint was four (ECFP4), which detects the multiple layers of the neighborhoods from the molecule centre, and all molecules were converted into 2048 fixed-length bit string. Overall, the input has 2049 features, from which 2048 are binary variables from ECFP, and one from the residual depth.