De novo methods to generate molecules with specific properties have been pursued since the early 1990s but with limited success, primarily due to many of the generated molecules being too difficult to synthesize. We propose to create a system that can start from a specific hit (that can either be suggested by the user or selected from an HTS) and generate new synthesizable molecules with optimized binding profiles. We take advantage of recent developments in ML methods for molecular generation and binding energy (BE) estimation. This will be the first ML-based tool for the complete hit identification and optimization process.