a) De novo drug generation. We have developed an in-house program that is designed to generate chemically rational molecules by placing atoms in the receptor one by one and connecting them by bonds based on standard force field, while optimizing their interactions to a target macromolecule (usually a protein). It is designed to work in any parametrized chemical environment and explores vast regions of chemical space. Given the structure of the protein in PDB and GRO formats with residue names denoting a particular hotspot, the program picks a random location within the hotspot and starts generation with a seed atom. A second atom is chosen to bind to this seed atom and places it within the bonding sphere of the first. Then a third atom is chosen and is placed within the bonding sphere of the second and within the base of a cone formed by the first three atoms. The fourth atom is placed following the allowed dihedral angle of the first four atoms, and the other rules for bonding and bending angles are mentioned for the first three atoms. Thus, the construction of the entire molecule follows one atom at a time following the restrictions of a given classical force field and applying the approach of similar to the Configurational Bias Monte Carlo (CBMC) until a molecule of a given target size is completed. Since we follow a particular classical force field (here, the program has been fine-tuned to work with the CHARMM-27 ), we use atom-type instead of the atom, which provides both bonding and nonbonding interactions within itself and with the protein. Based on the force field parameters our program can calculate the nonbonded interaction energies between any two (or more) molecules. This interaction energy is used for optimizing the molecules generated within a given region, i.e., hotspot. This is rather simplistic for a scoring function. However, for biasing a rapidly growing molecule, it is a good starting point. A quick local optimization is performed using gradient descent to get a more accurate score for the molecule. There is a scope to add in more filters at this stage to make the final screening more accurate, which the algorithm allows as an external input. A brief intuition of this algorithm can be gleaned from the flowchart shown in the supplementary information (SI) Fig. . We applied our program to target RdRp, the structure of which is obtained from the Protein Data Bank (PDB ID: 6NUR). We used Modeller 9.21 to model missing residues in the protein.