Comprehensive Chemical Space Exploration in De Novo Drug Design: An Algorithmically Constrained, Parallel Dual-Paradigm Framework Validated on BACE1
Year: 2026
Type: preprint
Abstract: Isolated de novo drug design paradigms carry inherent vulnerabilities: ligand-based (LBDD) models risk unreliable extrapolation beyond their training domain, while structure-based (SBDD) genetic algorithms accumulate molecular size-bias across evolutionary generations. Furthermore, on structurally complex targets, deploying either paradigm in isolation risks systematically excluding viable chemical space. Here, we introduce a dual-paradigm framework addressing both failure modes through two novel algorithmic constraints: a Zoned Applicability Domain (AD) integrated as a live reinforcement learning reward gradient, and the Size-Independent Ligand Efficiency (SILE) metric deployed as a generational fitness function. A convergent validation cascade - pharmacokinetic filtering, exhaustive docking, and molecular dynamics - is architecturally positioned to eliminate artifacts these constrained engines may still produce. We evaluated this workflow on β-secretase (BACE1), deliberately selected because its electrostatic catalytic dyad and hydrophobic sub-pockets present divergent reward landscapes. Ablation studies confirmed the Zoned AD suppressed out-of-domain artifacts to enrich extrapolation, while SILE prevented meaningless molecular bloat compared to baseline models. The LBDD pipeline autonomously rediscovered 10 withheld experimental inhibitors, while the SBDD pipeline successfully decoupled docking affinity from molecular size, with both yielding leads with thermodynamic profiles comparable to the clinical reference verubecestat. Critically, the campaign demonstrated a fundamental algorithmic divergence: LBDD generates data-driven compounds inheriting the binding topology of known inhibitors, while SBDD explores pocket-complementary space with substantially different binding mechanisms. This suggests that parallel deployment of constrained paradigms may be necessary for systematic coverage of a target's viable binding space.
Source: ChemRxiv
Authors: Thang T. Dang, Mai N. Luong
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7213225171