Drug Screening
COMPUTATIONALThis pipeline computationally filters thousands of ChEMBL compounds down to the best candidates for SMA drug discovery. The process runs in six steps: (1) ChEMBL query — compounds bioactive against top-scored SMA targets are fetched with their SMILES strings; (2) RDKit descriptor calculation — molecular weight, LogP, rotatable bonds, H-bond donors/acceptors, TPSA, and QED are computed from SMILES; (3) Lipinski Rule of 5— MW < 500, LogP < 5, HBD ≤ 5, HBA ≤ 10; (4) BBB permeability estimate— TPSA < 90 Ų and MW < 450; (5) CNS MPO score — 0–6 composite tuned for CNS drug development; (6) PAINS filter — substructure alerts for pan-assay interference compounds.
Why BBB penetration matters for SMA:SMA motor neurons reside in the anterior horn behind the blood-brain barrier. Risdiplam succeeds partly because of its BBB-permeable profile. Compounds with TPSA > 90 Ų or MW > 500 Da are unlikely to achieve meaningful CNS exposure.
Score glossary: Lipinski — binary pass/fail. BBB — heuristic estimate (TPSA + MW). CNS MPO — 0–6; ≥ 4 is CNS-optimized. QED — 0–1 drug-likeness; ≥ 0.5 is high quality. PAINS — substructure alert for reactive scaffolds.
▶How does Computational Drug Screening work?
The screening library starts from ChEMBL compounds with known bioactivity against SMA-relevant targets. Compounds pass through sequential filters:
- Lipinski Ro5 — MW ≤500, LogP ≤5, HBD ≤5, HBA ≤10
- BBB heuristic— TPSA < 90 Ų, MW < 450
- CNS MPO ≥ 4 — composite CNS optimization score
- QED ≥ 0.5 — drug-likeness estimate
- PAINS-free — no pan-assay interference alerts
* QED, CNS MPO, and PAINS columns are estimated values (heuristic from pChEMBL, MW/LogP thresholds). Not RDKit-computed. Use /screen/smiles for exact values.