Graph–theoretic degree–based descriptors play a central role in chemoinformatics and QSPR/QSAR modelling, yet most classical indices either focus purely on vertex degrees or treat bond contributions in a purely multiplicative way. In this work we introduce and systematically study a new family of modified bond-based indices in which each edge \(uv\in E(G)\) is weighted by a local bond factor \(\mathcal {T}(uv)={\phi _G(\textrm{u})}+{\phi _G(\textrm{v})}-2\) in the denominator, coupled with a vertex kernel in the numerator. This construction yields modified versions of the first and second Zagreb indices, the Forgotten and Yemen indices, several connectivity-type descriptors (product, sum, Nirmala, ABC, CAB, GA, harmonic, and misbalance prodeg), as well as Sombor- and Dharwad-type bond indices. We first present a unified edge–partition representation for any symmetric kernel, expressing each modified index as a finite sum over degree classes \(E_{(a,b)}(G)\) . This framework allows us to derive closed-form expressions for all sixteen modified bond-based indices on a broad collection of benchmark families: paths \(P_{\textrm{n}}\) , cycles \(C_{\textrm{n}}\) , complete graphs \(K_{\textrm{n}}\) , complete bipartite graphs \(K_{m,\textrm{n}}\) , stars \(S_{\textrm{n}}\) , friendship graphs \(F_{\textrm{n}}\) , wheels \(W_{\textrm{n}}\) , book graphs \(B_{\textrm{n}}\) , Dutch windmill graphs \(D_{\textrm{n}}^{(m)}\) , and hypercubes \(Q_d\) . The resulting tables reveal clear asymptotic growth patterns and highlight which structures are extremal for the modified descriptors. Moreover, we obtain sharp degree–extreme bounds for a representative subset of the indices in terms of the order \(\textrm{n}\) , size m, and the minimum and maximum degrees \(\delta\) and \(\Delta\) , with equality characterizing regular graphs. The proposed modified bond-based indices thus provide a flexible and analytically tractable family of descriptors that couple vertex and bond information in a novel way, and are well suited as structured features for modern chemoinformatics and graph-based machine-learning models on molecular graphs. Finally, to demonstrate predictive utility in a hypothesis-driven setting, we further benchmark these \({}^{m}\textrm{BI}\) descriptors within a large multi-task QSAR/QSPR pipeline on 3,219 ChEMBL antibacterial molecules across ten continuous properties using a heterogeneous model zoo under three descriptor scenarios, where the combined descriptors scenario achieves the best overall generalisation (Macro Test \(R^2 = 0.861\) ; Global zRMSE \(= 0.373\) ), improving upon the Physicochemical descriptors scenario (Macro Test \(R^2 = 0.852\) ; Global zRMSE \(= 0.385\) ).
