PMC:4331677 / 13007-14455 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4331677","sourcedb":"PMC","sourceid":"4331677","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4331677","text":"As described above, all binding sites could be described as a matrix S = (s1, s2,⋯,sm)T, where m is the number of binding sites. si = (si1, si2,⋯,sip)T denotes the i-th binding site, where sik corresponds the k-th fragment of the i-th binding site and p is the number of site features/fragments. Meanwhile, all ligands could be described as a matrix L = (l1, l2,⋯,ln)T, where n is the number of ligands. lj = (lj1, lj2,⋯,ljq)T denotes the j-th ligand, where ljk indicates the presence or absence of ligand the k-th substructures of the j-th ligand and q is the number of ligand features. The interaction data set is denoted as D = {(i1, j1), (i2, j2),⋯,(ic, jc)}, where ik = 1, 2,⋯,m; jk = 1, 2,⋯,n; (ik, jk) is the k-th site-ligand pair and c is the number of site-ligand interaction pairs. sik and ljk are the site and ligand vectors in the k-th interaction pair respectively. y = (y1, y2,⋯,yc) denotes the labels of interaction pairs, where yk ∈ {1, −1} indicate the positive and negative interaction respectively. Because fragments of targets and ligands are in a physically interaction distance (within 8 angstrom), it is reasonable to assume that there exist inherent chemical interactions between target and ligand features, and the sum of feature interactions determinates the target-ligand interaction. Therefore, the proposed approach is called fragment interaction model (FIM, Figure 1) and it can be expressed as the following equation:","tracks":[]}