¿Puede la IA reemplazar el 60% de la I+D farmacéutica diseñando y probando nuevos fármacos *in silico* mediante química generativa y modelos predictivos de toxicidad ?

Los modelos de aprendizaje profundo como AlphaFold ya han revolucionado el plegamiento de proteínas. La IA generativa ahora propone moléculas novedosas con afinidades de unión prometedoras, lo que plantea la pregunta de cuándo la IA podrá asumir por completo el descubrimiento de fármacos.

Background

As of 2024, AI-driven generative chemistry and predictive toxicity models have made significant strides in accelerating early-stage drug discovery, enabling rapid in silico design and screening of molecular candidates. Techniques such as multi-objective optimization with reinforcement learning (e.g., REINVENT or MolGen) and transformer-based models (e.g., AlphaFold2-informed docking) can propose novel structures with favorable binding affinities and reduced off-target risks. Deep learning models like AlphaFold have already revolutionized protein folding. However, no published source supports the claim that these tools can autonomously replace 60% of traditional pharmaceutical R&D—clinical trials, regulatory filings, and large-scale human trials remain human-led and data-intensive. Current industry practice emphasizes AI as a force multiplier in hit discovery and lead optimization rather than a wholesale replacement of R&D workflows.