Akademik Veri Yönetim Sistemi
International journal of advances in engineering and pure sciences (Online), cilt.38, sa.1, ss.210-230, 2026 (TRDizin)
Computational screening in drug discovery typically concentrates on a single “intended” target; yet as projects approach the clinic, unexpected liabilities—off-target binding, promiscuous (multi-target) proteins, targets implicated in adverse and side effects, and those monitored in safety panels (e.g., Eurofins SafetyScreen™ tiers 1–3)—often drive failure. In order to address such a challenge, this study addresses the lack of a unified resource that enables early, collective checks against such risky targets. We surveyed the literature and public databases, compiling 49 lists organized into five groups: (1) off-target, (2) promiscuous target, (3) adverse-effect target, (4) side-effect target, and (5) safety-check target. Target identifiers were standardized to UniProtKB, and each list was internally ranked using volume and study-specific scores. Reciprocal Rank Fusion (RRF) has been applied to merge these heterogeneous rankings into a single, robust ordering—RRF rewards targets that rank highly across multiple sources, elevating consistently implicated proteins to the top. The resulting resource, “De-TarDis” (Decoy-Target Discovery Database), yields a consolidated “avoid-these-targets” list for computational campaigns. It can be used directly during hit-to-lead and lead-optimization to flag compounds likely to bind safety-relevant proteins, thereby reducing late-stage, ADMET-driven surprises.
Computational screening in drug discovery typically concentrates on a single “intended” target; yet as projects approach the clinic, unexpected liabilities—off-target binding, promiscuous (multi-target) proteins, targets implicated in adverse and side effects, and those monitored in safety panels (e.g., Eurofins SafetyScreen™ tiers 1–3)—often drive failure. In order to address such a challenge, this study addresses the lack of a unified resource that enables early, collective checks against such risky targets. We surveyed the literature and public databases, compiling 49 lists organized into five groups: (1) off-target, (2) promiscuous target, (3) adverse-effect target, (4) side-effect target, and (5) safety-check target. Target identifiers were standardized to UniProtKB, and each list was internally ranked using volume and study-specific scores. Reciprocal Rank Fusion (RRF) has been applied to merge these heterogeneous rankings into a single, robust ordering—RRF rewards targets that rank highly across multiple sources, elevating consistently implicated proteins to the top. The resulting resource, “De-TarDis” (Decoy-Target Discovery Database), yields a consolidated “avoid-these-targets” list for computational campaigns. It can be used directly during hit-to-lead and lead-optimization to flag compounds likely to bind safety-relevant proteins, thereby reducing late-stage, ADMET-driven surprises.