Biotechnology, cannabis medicine, and rapid advancements in artificial intelligence (AI) and machine learning (ML) are beginning to converge, according to a report in Cannabis Industry Journal. Hemant Kumar Bid, program director for the Master of Science in Biotechnology and the Medical Cannabis concentration at Morehouse School of Medicine, believes this convergence could dramatically accelerate the development of cannabis-based therapies.
Bid says AI could move cannabis medicine beyond today’s trial-and-error approach toward more personalized, precise treatments. “Modern biotechnology focuses on molecular biology, genetic engineering, and advanced cell culture techniques. This is where it will advance cannabis medicine,” Bid explains in the report.
AI refers to computer systems that perform tasks usually requiring human intelligence, such as reasoning, pattern recognition, and decision-making. Machine learning, a branch of AI, identifies patterns in large datasets, improves as it processes more data, and predicts future outcomes.
Many AI systems rely on deep learning, which uses neural networks to detect complex patterns across massive datasets. Natural language processing (NLP) allows computers to understand and generate human language and analyze vast bodies of scientific literature.
Machine learning is already widely used in biotechnology to analyze complex biological data and predict how molecules behave in the body.
Traditionally, drug discovery depends heavily on animal models and laboratory testing, a slow and expensive trial-and-error process. Machine learning can now analyze large molecular datasets to predict which cannabinoid compounds interact with specific receptors, including CB1, CB2, TRPV1, GPR55, and serotonin receptors.
This capability can significantly reduce both the time and cost of traditional drug discovery. AI can also predict toxicity and safety risks in drug formulations before human testing begins.
“Once a clinician knows that toxicity will exceed 70%, they can decide not to move forward,” says Bid in the report.
NLP tools can mine extensive cannabis research literature to identify emerging therapeutic targets and gaps in current studies. Deep learning systems can also analyze pathology images and genetic data at a level beyond human capability.
These systems can detect subtle changes in DNA, RNA, or protein structures that influence disease progression and treatment outcomes.
Machine learning models are already being applied to pharmacogenomic data to predict how a patient will metabolize cannabinoids based on genetic variation, particularly in liver-metabolizing enzymes like cytochrome P450, which play a key role in metabolizing THC and CBD.
“That means this genomics and pharmacogenomics approach, using machine learning models, could help predict efficacy and adverse effects before treatment begins,” says Bid.
AI and ML systems can analyze genetic mutations, disease risk, and patient data to suggest individualized treatment plans.
Mobile applications like Strainprint are already tracking cannabis use and therapeutic outcomes, collecting real-world patient data on dosing, product type, symptoms, and treatment results.
AI can use this data to predict treatment response, and such datasets may help researchers identify patterns of endocannabinoid dysfunction and develop standardized diagnostic tools.
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