Near real time (max 100 times slower then real time) differentiable, stochastic multi organ simulations with chemically accurate time and environment depending

Near real time (max 100 times slower then real time) differentiable, stochastic multi organ simulations with chemically accurate time and environment depending dynamic structure changes at all possible binding targets or interactions with body own components and third party drugs. Without machine learning at every atom is dynamic precision we are at 10^-18 L (liters) at 20 micro seconds a week with a specialised super computer https://dl.acm.org/doi/abs/10.1145/3458817.3487397 https://dl.acm.org/doi/abs/10.1145/3458817.3487397 . A solution does not need that precision everywhere. However a machine learning proxy of such precision in every relevant environment including 2d surface along non mixing fluid etc for every likely type of interaction is required so we can be certain of the possible outcomes. That would allow humanity to pre-screen a bunch of edge conditions and check for unintended or previously explained side effects. The derived surrogates for environment dependent reaction rates could be used in a spatially distributed event based simulations with level of precision ranging from atoms with position and electrons in orbits subject to electro-magnetic force interaction, molecules as things with position and rotation and folding state, concentration gradients of those as stochastic 3d PDEs, 2d PDEs, 1d PDEs and ODEs of the number of moles with relevant boundary conditions. If we had those reaction rates down and knew of all the proteins and other structures i am positive that a proxy model of relevant parts of the human body could achieve enough accuracy to be practical at pre-screening drugs with todays super computers.