TargetDock-AI: AI for Docking and Targeting Systems

• TargetDock-AI refers to AI, ML, and optimization methods used for target-directed docking and control in both virtual molecular systems and physical robotics.
• Key applications include scalable protein-ligand docking for drug discovery and optimization-based motion planning for autonomous vehicle and spacecraft docking.
• These methods focus on reliable, efficient, and often explainable decision-making to achieve optimal spatial alignment between a chaser and a target under various constraints.

TargetDock-AI encompasses a range of AI, machine learning, and optimization methodologies for target-directed docking and control in both physical robotics/automation and virtual molecular systems. The term refers most directly to two contemporary architectures: (1) general frameworks and benchmarks for evaluating protein–ligand docking algorithms at scale, especially in the context of distinguishing actives from inactives for drug discovery (2506.20043); and (2) advanced motion planning and control pipelines for automatic, collision-free docking of autonomous vehicles such as surface vessels, spacecraft, and robotic turrets (2004.07793, 2303.09619, 2410.12703, 2411.07550, 2408.16923). Each implementation shares a focus on reliable, efficient, and explainable decision-making for placing a “chaser” (physical or virtual) into optimal spatial relation to a designated “target,” subject to constraints on safety, validity, physical plausibility, and system resources.

1. Large-Scale Protein–Ligand Docking with TargetDock-AI

The TargetDock-AI dataset [Editor’s term] provides a comprehensive, activity-labeled benchmark for scalable evaluation of protein–ligand docking algorithms (2506.20043). It is specifically curated to address limitations in previous benchmarks regarding real-world discrimination of active versus inactive compound–protein pairs, a capability essential for high-throughput virtual screening (HTVS) and structure-based drug discovery (SBDD).

Dataset composition and construction:

• 563,251 protein–ligand pairs encompassing:
  • 1,446 “active” pairs (PubChem-confirmed binding)
  • 15,211 “inactive” pairs (experimentally non-binding)
  • 237 proteins (mainly neuroblastoma targets, AlphaFold predictions)
  • 2,584 FDA-approved small molecules
  • Remaining pairs are unlabeled
• Annotations derived from primary PubChem bioactivity data
• Dataset purposely exceeds the scale of earlier activity-labeled docking benchmarks

Evaluation task:

• Each docking algorithm is assessed for its ability to assign more favorable predicted binding affinities (lower scores) to “active” pairs relative to “inactive” pairs.

Technical pipeline (PocketVina as reference implementation):

1. Pocket detection via P2Rank: Surface grid points are scored for “ligandability” using atom environment descriptors and a random forest, clustered into putative binding pockets.
2. Search-based docking in each predicted pocket with QuickVina 2-GPU 2.1: For every ligand–protein pair, exhaustive conformational sampling is performed:

$$C = \{ x, y, z, a, b, c, d, \psi_1, ..., \psi_{N_{\text{rot}}} \}$$

where ($x, y, z$) are translation, ($a, b, c, d$) are orientation quaternions, and $\psi_i$ are ligand torsions.

• Scoring: The best (most negative) binding affinity score per pocket is assigned to each pair:

$$\text{SF}_C = f(C) = e_{\text{inter}} + e_{\text{intra}}$$

with $e_{\text{inter}}$ the protein–ligand interaction (precomputed grid) and $e_{\text{intra}}$ ligand internal strain.

Key findings for algorithm performance:

Aspect	PocketVina (Search-based)	CompassDock (DL-based)
Runtime	~3 days (7 GPUs, ~6 GB RAM)	~1.5 months (20 GPUs, ~15 GB)
Discriminates actives/inactives	Yes (p = 1.34e-82, directionally correct)	No (AA-Score p = 0.403, other metrics inconsistent)

• PocketVina demonstrates accurate and computationally efficient active/inactive discrimination at scale, outperforming the DL+physics baseline (CompassDock/DiffDock-L) both in accuracy and resource use.
• Evaluation is robust to new targets/ligands, as PocketVina requires no task-specific training.

Significance:

TargetDock-AI defines a new practical standard for evaluating SBDD pipelines, establishing that systematic pocket-conditioned, search-based docking can reliably stratify experimental outcomes across hundreds of thousands of protein–ligand combinations without retraining or target-specific bias.

2. Optimization-Based Planning and Control in Robotic Docking

In the context of physical docking and robotics, TargetDock-AI represents integrated control architectures for achieving safe, efficient, and autonomous coupling of mobile platforms with designated targets (2004.07793, 2203.00369, 2303.09619, 2410.12703, 2411.07550).

Typical system architecture (ASV example (2004.07793)):

1. Optimization-based trajectory planner: A nonlinear optimal control problem (OCP) generates collision-free, dynamically feasible trajectories considering static obstacles and all actuator/state limits:

$$\min_{x_p(\cdot), u_p(\cdot), s(\cdot)} \int_{t_0}^{t_0+T} \left( F(x_p(t), u_p(t)) + k_s^\top s(t) \right) dt$$

subject to vessel dynamics, spatial constraints (e.g., convex polygons for obstacles), slack for soft constraint violation, and actuation bounds.

1. Dynamic positioning controller: A PID-based feedback controller (with feed-forward) tracks time-parameterized trajectory references, compensating for unmodeled drift or disturbances.
2. Cascaded operation: Low-frequency trajectory generation feeds high-frequency closed-loop control, yielding robust, real-time, and collision-averse docking even in dynamic harbor or orbital environments.

Key properties:

• Model-based planning with formal constraints yields strict guarantees of obstacle avoidance and actuator feasibility.
• Feedback loop separation adds operational safety and resilience under real-world uncertainties.
• Full-scale experiments (e.g., on the milliAmpere autonomous ferry) show consistent, collision-free arrivals at the defined docking pose.

3. Learning and Explainability in Autonomous Docking

TargetDock-AI architectures also encompass advanced learning-based approaches, especially for handling complex environments, dynamic obstacles, or the need for human-understandable decision traces.

Reinforcement Learning (RL) and Explainability (2203.00369, 2410.12703):

• Deep RL agents (e.g., trained with PPO) can learn effective continuous control policies (e.g., chaser thruster commands for CubeSat docking), outperforming traditional model-based guidance in uncertain or nonlinear conditions.
• For assurance and transparency, policies can be approximated by linear model trees (LMT), providing interpretable feature attributions for every control action:

$I_f = \frac{a_f x_f}{\sum_j |a_j x_j|}$

enabling real-time, per-decision auditability, important for regulatory and operational safety.

Imitation and Inverse Reinforcement Learning (IRL) (2411.07550):

• Maximum Entropy Deep IRL is used to learn a reward model from expert trajectories, capturing the implicit “intent” of expert docking behaviors in neural architectures that combine spatial (environment map) and kinematic features.
• The learned reward maps are employed by motion planners (e.g., RRT* or similar), resulting in docking policies that adapt to dynamic environmental configurations, emulating human-like behavior while optimizing implicit preferences (e.g., risk aversion, smoothness).
• Such approaches demonstrate generalization to unseen configurations and support online learning from continued operator demonstrations.

4. Vision-Based and Multi-Agent Docking Systems

In satellite and multi-robot docking (2303.09619), TargetDock-AI-style frameworks integrate:

• Real-time, on-board visual pose estimation using fiducial markers (e.g., ArUco, OpenCV-based EPnP) for uncooperative, tumbling targets.
• Pose fusion across multiple collaborating chasers, outlier rejection, and centralized nonlinear model predictive control for simultaneous multi-agent trajectory optimization.
• Explicit cost functions balancing position/orientation error, control effort, and collision constraints within shared workspace, with robust real-time optimization (e.g., via OpEn/PANOC).
• Architectural modularity and scalability to larger multi-agent constellations, enhancing robustness and mission efficiency in servicing, debris removal, or collaborative spacecraft operations.

5. Error Analysis and Assurance in Target-Based Automation

In AI-driven targeting or aiming systems (2408.16923), especially those using AI for automatic target recognition (ATR):

• The primary source of targeting error is the AI localization itself (e.g., Faster R-CNN centroid error), not the electromechanical controller.
• Standard metrics (confidence score, IoU, average precision, average recall) are meaningful predictors of hit probability and can be leveraged for system-level error budgeting and real-time gating or suppression of downstream actions.
• For assured precision, system architectures should threshold on detection metric quality, minimize centroid prediction error, and systematically calibrate or compensate AI-induced targeting bias.
• These findings demonstrate the critical importance of evaluating not just end-to-end targeting performance but the specific contribution of AI perception modules to overall operational effectiveness.

6. Impact and Future Directions

TargetDock-AI frameworks and datasets have advanced both the benchmarking of virtual screening algorithms and the deployment of robust, explainable decision-making in real-world robotic control systems. In molecular docking, systematic, search-based approaches on activity-labeled scales now provide demonstrably better generalization and discrimination than current deep learning pipelines without retraining. In autonomous and collaborative robotics, learning-based architectures are increasingly able to match or surpass handcrafted policies, with new methods for explaining, verifying, and adapting behaviors as environments change or scenarios increase in complexity.

Future work in this domain includes expanding activity-labeled datasets to span more challenging targets and molecules, real-world validation of deep IRL policies in variable natural environments, and continued development of hybrid (model-based/model-free) control architectures for multi-agent, high-assurance docking and targeting applications.