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Here, we introduce a versatile multiscale framework that directly connects molecular\u2010level electrostatics to collective properties via a colloid\u2010inspired coarse\u2010grained modeling combined with neural network\u2010assisted optimization. Using monoclonal antibodies as a model system, our inverse design approach identifies charge patterns capable of reliably reproducing experimental structure factors, osmotic compressibility and collective diffusion coefficients in a wide region of protein concentrations. By further inspecting our data, we find specific physical features and spatial arrangements of localized charge patches that significantly influence the solution structure, uncovering clear design principles. 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