The increasing use of medical marijuana highlights the importance of developing a better understanding of cannabinoid metabolism. Phytocannabinoids, including ∆ 9 -tetrahydrocannabinol (THC), are metabolized and inactivated by cytochrome P450 enzymes primarily within the liver. The lipophilic nature of cannabinoids necessitates mechanism(s) to facilitate their intracellular transport to metabolic enzymes. Here, we test the central hypothesis that liver-type fatty acid binding protein (FABP1) mediates phytocannabinoid transport and subsequent inactivation. Using X-ray crystallography, molecular modeling, and in vitro binding approaches we demonstrate that FABP1 accommodates one molecule of THC within its ligand binding pocket. Consistent with its role as a THC carrier, biotransformation of THC was reduced in primary hepatocytes obtained from FABP1-knockout (FABP1-KO) mice. Compared to their wild-type littermates, administration of THC to male and female FABP1-KO mice potentiated the physiological and behavioral effects of THC. The stark pharmacodynamic differences were confirmed upon pharmacokinetic analyses which revealed that FABP1-KO mice exhibit reduced rates of THC biotransformation. Collectively, these data position FABP1 as a hepatic THC transport protein and a critical mediator of cannabinoid inactivation. Since commonly used medications bind to FABP1 with comparable affinities to THC, our results further suggest that FABP1 could serve a previously unrecognized site of drug-drug interactions.

As a complement to virtual screening, de novo design of small molecules is an alternative approach for identifying potential drug candidates. Here, we present a new 3D genetic algorithm to evolve molecules through breeding, mutation, fitness pressure, and selection. The method, termed DOCK_GA, builds upon and leverages powerful sampling, scoring, and searching routines previously implemented into DOCK6. Three primary experiments were used during development: Single‐molecule evolution evaluated three selection methods (elitism, tournament, and roulette), in four clinically relevant systems, in terms of mutation type and crossover success, chemical properties, ensemble diversity, and fitness convergence, among others. Large scale benchmarking assessed performance across 651 different protein‐ligand systems. Ensemble‐based evolution demonstrated using multiple inhibitors simultaneously to seed growth in a SARS‐CoV‐2 target. Key takeaways include: (1) The algorithm is robust as demonstrated by the successful evolution of molecules across a large diverse dataset. (2) Users have flexibility with regards to parent input, selection method, fitness function, and molecular descriptors. (3) The program is straightforward to run and only requires a single executable and input file at run‐time. (4) The elitism selection method yields more tightly clustered molecules in terms of 2D/3D similarity, with more favorable fitness, followed by tournament and roulette.