Photodynamic therapy (PDT) is a targeted cancer treatment that uses light-activated photosensitizers to generate reactive oxygen species that selectively destroy tumor cells, generally causing less collateral damage than conventional treatments. However, its clinical success hinges on the availability of photosensitizers with strong optical sensitivity and high efficiency in generating reactive oxygen species. While classical computational methods have provided useful insights into photosensitizer design, they struggle to scale and often lack the accuracy needed for these simulations. In this work, we show how fault-tolerant quantum algorithms can help identify promising photosensitizer candidates for PDT. First, we introduce a threshold projection algorithm that estimates the dipole strength within the therapeutic window as a measure of optical sensitivity. Second, we use the intersystem crossing (ISC) evolution-proxy approach to estimate electronic singlet–triplet mixing, with a separate vibronic dynamics treatment when nuclear motion must be included. We apply these algorithms to a clinically relevant and actively pursued class of photosensitizers, BODIPY derivatives, including heavy-atom and transition-metal-substituted systems that are challenging for classical methods. For active spaces of 11–45 spatial orbitals, we estimate 278–497 logical qubits for the qubitized implementations. Two readouts per execution reduce the absorption sampling cost at the same combined filtering and statistical accuracy. These algorithms directly evaluate photophysical quantities that can guide photosensitizer design.