In　the　drug　discovery　process,　the　biological　activity　value　(BAV)　of　G　Protein-Coupled　Receptors　(GPCRs)　targeting　ligands　is　a　large　consideration.　Past　BAV　prediction　on　CPU　consumes　tremendous　time　and　power,　yet　there　is　rarely　any　related　acceleration　research.　Therefore,　this　paper　proposes　a　series　of　heterogeneous　FPGA-based　accelerators　for　well-performing　algorithms　to　predict　GPCRs　ligands　BAV.　Communication　delay　is　reduced　by　compressing　the　sparse　matrix　and　directly　coupling　accelerators　on　the　system　BUS.　Computation　is　accelerated　by　the　remapping　during　the　weight　storage.　Experimental　results　show　that　our　FPGA　accelerator　implemented　on　Xilinx　XCZU7EV　performs　54.5×　faster　than　CPU　and　35.2×　more　energy-efficient　than　GPU.　©　2022　IEEE.