TY - JOUR AU - Abbasi, Wajid Arshad AU - Yaseen, Adiba AU - Hassan, Fahad Ul AU - Andleeb, Saiqa AU - Minhas, Fayyaz Ul Amir Afsar PY - 2020 DA - 2020/11/25 TI - ISLAND: in-silico proteins binding affinity prediction using sequence information JO - BioData Mining SP - 20 VL - 13 IS - 1 AB - Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning. SN - 1756-0381 UR - https://doi.org/10.1186/s13040-020-00231-w DO - 10.1186/s13040-020-00231-w ID - Abbasi2020 ER -