“…Computational de novo drug design involves the use of techniques such as genetic algorithms, − reinforcement learning, including deep reinforcement learning, − generative deep learning models, − or other deep learning methods, e.g., graph transformers, , models that blend deep learning and evolutionary algorithms, ,, and string-based transformers (i.e., operating on a simplified molecular-input line-entry system (SMILES) string representation of molecules). , The algorithms “computationally synthesize” novel drug molecules, either by starting from scratch and adding atoms to form a novel molecule or by modifying or adding atoms to an existing chemical structure (“scaffold”). The result is the creation of novel molecules by (a) simulating chemical modifications that optimize for the single objective of improving binding efficiency to a target or (b) multiobjective optimization including drug-likeness objectives, e.g., solubility and other drug-likeness factors. ,− …”