Multi-eGO model of c-Src kinase:dasatininb and c-Src kinase:PP1: all-atom training simulations. To test the transferability of the protocol developed for LYZ:BNZ, we reapplied it to the binding of DAS and PP1 to SRC. First, we performed several training MD simulations for the apo system as well as for the SRC:DAS and SRC:PP1 holo systems, in both cases 4 in-solution ligands were added (see Methods and Table ). The training simulations showed different interactions between ligands and the protein in correspondence of known binding sites, as previous shown in ref. . Besides the ATP binding site, we found important interactions with the N-lobe (b-sheet), the αG helix, the P-loop, binding in the MYR site for both ligands and binding in the PIF site for PP1 (see Fig. ). Multi-eGO model of c-Src kinase:dasatininb and c-Src kinase:PP1: prior model simulations. To parameterize the multi-eGO models, after performing a prior simulation of SRC as a self-avoiding polymer, we set the 𝜀 ' parameter for the protein to match the radius of gyration (Rg), the residue-wise root-mean square fluctuations of the atomic positions (RMSF), and RMSD distributions, obtaining an optimal value of 0.33 kJ/mol (Fig. ). To describe intermolecular interactions, we first performed an intermolecular reference prior simulation at the same ligand concentration as the training simulation to account for random collisions between ligand molecules. We then optimized the corresponding 𝜀 ' and obtained a value of 0.43 kJ/mol and 0.6 kJ/mol for DAS:DAS and PP1:PP1, respectively, which could reproduce their training contact probabilities. A second intermolecular prior simulation was then performed in which the ligands were allowed to interact with each other, while the protein and ligands only interacted with hard-core repulsion. Finally, by minimizing the RMSE between the training and multi-eGO residue-wise intermolecular contact probabilities, we obtained an optimal 𝜀 ' of 0.32 kJ/mol and 0.35 kJ/mol for SRC:DAS and SRC:PP1, respectively (Fig. ). Binding simulation of c-Src kinase:dasatininb and c-Src kinase:PP1: multi-eGO models. The resulting multi-eGO models were used to perform 40 binding simulations for both PP1 and DAS, starting from a random conformation of four unbound ligands (at the same concentration as in training). To establish the binding, we chose three reference atoms of the protein, namely CD:GLU310, N:MET341, CA:THR338 for SRC:DAS and N:ILE294, N:MET341, N:THR338 for SRC:PP1, and we stopped each simulation when the three minimum distances between one of the ligands and the three reference atoms were below 0.5 nm, 0.4 nm, 0.5 nm for SRC:DAS and 0.5 nm, 0.35 nm, 0.5 nm for SRC:PP1. The calculated RMSD of the ligand to its bound conformation after alignment of the protein is shown in Fig. (see also SI Movie S2 and S3). Binding times (RMSD less than 0.26 nm) were fitted to a Poisson distribution, yielding mean binding times of 3.5±0.1 ns and 1.64±0.02 ns for SRC:DAS and SRC:PP1, respectively, with corresponding pvalues of 0.92 and 0.99992 obtained with KS-test (Fig. ). The corresponding kon for SRC:DAS and SRC:PP1 are 2.51x10 10 M -1 s -1 and 5.39x10 10 M -1 s -1 , respectively. Despite the fact that, as for LYZ:BNZ, the time scales are nominal and cannot be compared directly with experiments (i.e., the in vitro kon for DAS is ~5x10 6 M -1 s -1 ), their ratio is consistent with the ratio of those obtained by previously published MD simulations (1.9x10 6 M -1 s -1 and 4.3x10 6 M -1 s -1 for DAS and PP1, respectively), indicating for this case a speed up of around four order of magnitudes. Estimation of c-Src kinase:dasatininb and c-Src kinase:PP1 unbinding free energy via Thermodynamic Integration: multi-eGO models. Having shown that the model can simulate the correct binding of the two ligands, we estimated their unbinding free energy by TI, using three alternative restraints as before (see Methods and Fig. and). For each restraint we performed 4 TI replicates. For SRC:DAS we estimated an unbinding free energy of 55.0±0.5 kJ/mol, 55.5±0.3 kJ/mol , 55.6±0.4 kJ/mol for the three restraints, respectively (Figure ), while for SRC:PP1 we estimated an unbinding free energy of 38.34±0.06 kJ/mol, 38.4±0.1 kJ/mol, 38.3±0.1 kJ/mol (Figure ). As for LYZ:BNZ all estimates are compatible and show high statistical precision. Importantly, the ΔG obtained for SRC:DAS is compatible with values reported in the literature for in vitro measurements value , ranging from 55.17 kJ/mol to 60.23 kJ/mol (Figure ). For SRC:PP1, we compared the experimental ratio of the IC50 of SRC:DAS (0.5 nM) and SRC:PP1 (170 nM) with the ratio of the 𝐾 $ = exp ‡-GH I 0 J ‰ estimates obtained with multi-eGO. The ratio of the multi-eGO KD values was found to be in qualitative agreement with the IC50 ratio, namely Preparation of the Aβ42:10074-G5 multi-eGO model. The above binding processes represent the most common scenario where a pose is known, or hypothesized, under the lock-and-key mechanism, where the ligand minimally perturbs the structure of the receptor. At the other end of the spectrum is the case of ligand binding to IDPs, which do not entail a well-defined binding site due to the absence of a stable tertiary structure. To test multi-eGO in this scenario, we used the large publicly available dataset for Aβ42 simulated in its apo and 10074-G5 holo states . After parametrizing the Aβ42 monomer, as previously published, with an intramolecular 𝜀 ' of 0.33 kJ/mol, we parametrized the multi-eGO 10074-G5 starting from its GROMOS ATB-derived model and optimized its bonded parameters to match those of the training simulation (see Methods). We then performed a reference prior simulation in which mutual interactions between Aβ42 and 10074-G5 are represented only by steric clashes in the same training condition. After this simulation, we set the intermolecular interaction between Aβ42 and 10074-G5 by minimizing the RMSE of the intermolecular residue-wise contact maps (see Fig. ), obtaining a value of 𝜀 ' of 0.385 kJ/mol. The multi-eGO holo-simulation showed the same Rg behavior observed in the training simulation (Fig. ), qualitatively reproducing the same peak shift and narrowing of the distribution. Note that this effect is only due to ligand binding, since the intramolecular interactions are only learned from the apo simulation. Single-molecule simulation of the Aβ42:10074-G5 multi-eGO model. With the parameters found above, we ran a 2 µs multi-eGO MD simulation and calculated the probability of binding vs. unbinding to estimate the KD. In the case of single molecule simulations, direct estimation of KD as