A computational model of the Drosophila brain, based on its connectome and neurotransmitter identity, reveals sensorimotor processing. The model, built using a leaky integrate-and-fire approach, simulates the brain's circuitry to study feeding and grooming behaviors. Activation of gustatory neurons in the model accurately predicts taste-responsive neurons involved in feeding initiation. The model also predicts motor neuron firing in response to feeding-related stimuli, validated by optogenetic and behavioral experiments. It demonstrates how different taste modalities interact at the circuit level, providing insights into aversive and appetitive taste processing. The model was applied to mechanosensory circuits, accurately predicting the antennal grooming circuit and describing responses to different mechanosensory inputs. The model's predictions were validated through experiments, showing its ability to generate testable hypotheses and describe sensorimotor transformations. The model successfully predicts neurons involved in proboscis extension, including those required for sugar and water detection. It also identifies neurons that inhibit proboscis extension, such as Ir94e neurons. The model's accuracy is supported by experimental validation, demonstrating its utility in understanding complex neural circuits. The model's predictions align with experimental findings, highlighting its potential for studying sensorimotor processing in the Drosophila brain. The model's success underscores the value of computational approaches in elucidating neural circuitry and sensory processing in complex networks.A computational model of the Drosophila brain, based on its connectome and neurotransmitter identity, reveals sensorimotor processing. The model, built using a leaky integrate-and-fire approach, simulates the brain's circuitry to study feeding and grooming behaviors. Activation of gustatory neurons in the model accurately predicts taste-responsive neurons involved in feeding initiation. The model also predicts motor neuron firing in response to feeding-related stimuli, validated by optogenetic and behavioral experiments. It demonstrates how different taste modalities interact at the circuit level, providing insights into aversive and appetitive taste processing. The model was applied to mechanosensory circuits, accurately predicting the antennal grooming circuit and describing responses to different mechanosensory inputs. The model's predictions were validated through experiments, showing its ability to generate testable hypotheses and describe sensorimotor transformations. The model successfully predicts neurons involved in proboscis extension, including those required for sugar and water detection. It also identifies neurons that inhibit proboscis extension, such as Ir94e neurons. The model's accuracy is supported by experimental validation, demonstrating its utility in understanding complex neural circuits. The model's predictions align with experimental findings, highlighting its potential for studying sensorimotor processing in the Drosophila brain. The model's success underscores the value of computational approaches in elucidating neural circuitry and sensory processing in complex networks.