# Multi-objective reinforcement learning

Generative models for target-based drug design have long compressed protein–ligand interactions into a single docking score, hiding functional differences between binding modes on the same target. BindMol clusters the binding pocket into hotspots from reference complexes, assigns each hotspot its own reward dimension, and optimizes the conflicting objectives with preference-conditioned multi-objective reinforcement learning. On parp1, fa7, 5ht1b, braf and jak2 its novel hit ratios are 42.617, 24.580, 42.149, 30.767 and 44.864, above GEAM at 40.635, 17.903, 36.667, 27.887 and 42.025, and it recovers the crystallographic type-I and type-II binding modes more often on braf and jak2.

Generative models for target-based drug design have long compressed protein–ligand interactions into a single docking score, hiding functional differences between binding modes on the same target. BindMol clusters the binding pocket into hotspots from reference complexes, assigns each hotspot its own reward dimension, and optimizes the conflicting objectives with preference-conditioned multi-objective reinforcement learning. On parp1, fa7, 5ht1b, braf and jak2 its novel hit ratios are 42.617, 24.580, 42.149, 30.767 and 44.864, above GEAM at 40.635, 17.903, 36.667, 27.887 and 42.025, and it recovers the crystallographic type-I and type-II binding modes more often on braf and jak2.

The work proposes MO-IKE, a multi-objective reinforcement learning algorithm that formulates prompt construction for in-context knowledge editing as a Constrained Markov Decision Process and trains a dynamic retriever to optimize competing objectives of reliability, generality, and specificity, improving edit success on Llama-3.2 from 85.0% to 92.0%, paraphrase consistency from 77% to 79%, and retention rate by 23.0% over prior RL-based methods.

The work proposes MO-IKE, a multi-objective reinforcement learning algorithm that formulates prompt construction for in-context knowledge editing as a Constrained Markov Decision Process and trains a dynamic retriever to optimize competing objectives of reliability, generality, and specificity, improving edit success on Llama-3.2 from 85.0% to 92.0%, paraphrase consistency from 77% to 79%, and retention rate by 23.0% over prior RL-based methods.