Calibration. The identification of optimal parameters for the reactive docking forcefield was done by performing an exhaustive parameter sweep for ε, W1, 3, and W1, 4 values, optimizing the docking performance on the 80 complexes in the training set. The calibration showed multiple sets of values could achieve comparable success rates, with the ε being the dominant component. In fact, the analysis of the results shows accuracy starts dropping significantly with ε values lower than 2.0 Kcal/mol, below which, scaling factors have limited to no effect. Therefore, the optimal values maximizing success rates and minimizing false positive rates were ε = 3.5 kcal/mol, W1, 3 = 0.8, W1, 4 = 0.4. These parameters achieved an overall success rate across the training set of 95% in identifying the correct residues as the top scored result, and >95% in the top 3 results (Fig. ). Predictions of reactions for cysteines with acrylamides and chloroacetamides, as well as serines with boronic acids, showed excellent performance (95% top score, in all cases), while predictions of reactions between serines and β-lactams performed slightly worse (80%) (Fig. ). Due to the higher relative abundance of solvent-accessible serines (693, approximate reactive hit ratio 1:17) over cysteines (186, approximate reactive hit ratio 1:4, Figs. ) in the proteins of the training set, we anticipated that predicting the former would be a more challenging task, but overall success rates remained high for both residues. Testing of selected parameters. The performance of the optimal values obtained in calibration was tested on the 351 complexes in the test set, which is larger and significantly more diverse than the training set. This set includes all residue types and warheads in the training set and extends it with more well-represented warhead/residue combinations (e.g., 𝜶,𝛃 unsaturated carbonyls like cyano acrylamides, vinyl carbonyl, phosphates for cysteines) as well as residues and warheads for which it was not possible to obtain a representative number of complexes. While the overall performance in identifying the correct residue as the top result dropped from 95% of the training set to 86.6% (Fig. ) for the test set, all results showed a consistent performance of at least 75% success rate or better, except for carbonyl warheads targeting lysines (62%, Fig. ). The more marked drop for this residue is likely due to a combination of its high abundance in the proteins considered (14 lys/system, Fig. ) and their preferred localization in highly solvent-accessible regions on the surface of the protein. Both factors concurred in making docking predictions more challenging due to the potentially higher rate of false positives and the lack of well-defined pockets. Virtual screening. The performance of the reactive docking was then validated against experimental results reported on a small covalent ligand discovery effort in a virtual screening (VS) setting. For that, a virtual screening set was built based on the experimental data published by Resnik et al. which used a library of 993 reactive fragments functionalized with mild electrophiles including chloroacetamides (n= 752, 76%) and acrylamides (n= 241, 24%) Fig. . The library was used to screen for binders of 10 cysteine-containing protein targets, using intact-protein MS and high-throughput crystallography to identify and characterize hits. The experimental characterization identified two classes of hits: strong hits (>50% labeling) and weak binders (<50% labeling) (Fig. ). The virtual screening set used here contains 3 of the 10 targets from the paper for which structures were available and potent and selective fragments were identified: two enzymes (deubiquitinase OTUB2; pyrophosphatase NUDT7), and K-Ras G12C . For the validation, the set of parameters obtained from the calibration step was used to dock the entire fragment library against the targets in a blind docking fashion, considering all solvent-accessible cysteines for each target and predicting both the most likely residue to be modified and the most likely ligands to react (Table ). For the first task, the method was able to detect the correct cysteines in all three targets, confirming its residue prediction capabilities. For the VS task, we then analyzed the docking results considering the top 0.5%, 1.0%, and 10% of the results ranked by docking score for the different warheads, as summarized in Fig. . The method achieved true positive hit rates for top 0.5% of ranked results of 100% on NUDT7 chloroacetamide binders (50% strong and 50% weak binders) and 50% on OTUB2 chloroacetamide binders (25% strong and 25% weak binders). For KRAS, the true positive rate at the top 0.5% was only 25%. Acrylamide fragments were more difficult to predict correctly given the lower number of representatives of this warhead in the library, as well as the smaller number of strong binders. Combined success rates at identifying binders of both chemical classes in the top 0.5% of ranked results were 40% (20% strong and 20% weak binders) for OTUB2, 40 % (weak binders) for NUDT7 and 40% (weak binders) for KRAS.