The growing support for noncanonical amino acids in structure prediction tools such as AlphaFold3 has been largely facilitated by the Chemical Component Dictionary (CCD). However, the limited coverage of modified residues in CCD continues to restrict the application of these models to many biologically and therapeutically relevant peptides. To address this gap, we present HighRes_Builder, a computational method for efficient residue search and automated construction of noncanonical amino acids not currently archived in CCD. We demonstrate the utility of our approach by predicting structures for 3179 noncanonical residues beyond the CCD using AlphaFold3. AlphaFold3 achieved 100% acceptance for both the noncanonical residue monomers and their corresponding 'GGXGG' motifs (where X denotes the noncanonical residue). Of these, 72.44% of the predicted residue monomer structures concurrently satisfy all five geometric criteria (d_N_C1, d_Ck_Ccarb, d_Ccarb_O_mean, ang_Ck_Ccarb_O_mean, and ang_O1_Ccarb_O2). Furthermore, among the generated motif structures, 85.78% exhibited favorable ω values for the embedded noncanonical residues. Furthermore, by integrating HighRes_Builder with structure prediction systems, AlphaFold3 for linear peptides and HighFold3 for cyclic peptides, we successfully model the conformation of the linear peptide drug Relamorelin and the cyclic therapeutic peptides LUNA18 and JNJ-77242113 in complex with their target proteins, elucidating structural determinants of their mechanism of action. This work establishes a scalable and accurate framework for structure prediction of diverse nonstandard peptides, highlighting its potential to accelerate rational design of peptide-based therapeutics.