Accurate Reproduction of 161 Small-Molecule Complex Crystal Structures using the EUDOC Program: Expanding the Use of EUDOC to Supramolecular Chemistry

EUDOC is a docking program that has successfully predicted small-molecule-bound protein complexes and identified drug leads from chemical databases. To expand the application of the EUDOC program to supramolecular chemistry, we tested its ability to reproduce crystal structures of small-molecule complexes. Of 161 selected crystal structures of small-molecule guest-host complexes, EUDOC reproduced all these crystal structures with guest structure mass-weighted root mean square deviations (mwRMSDs) of <1.0 Å relative to the corresponding crystal structures. In addition, the average interaction energy of these 161 guest-host complexes (−50.1 kcal/mol) was found to be nearly half of that of 153 previously tested small-molecule-bound protein complexes (−108.5 kcal/mol), according to the interaction energies calculated by EUDOC. 31 of the 161 complexes could not be reproduced with mwRMSDs of <1.0 Å if neighboring hosts in the crystal structure of a guest-host complex were not included as part of the multimeric host system, whereas two of the 161 complexes could not be reproduced with mwRMSDs of <1.0 Å if water molecules were excluded from the host system. These results demonstrate the significant influence of crystal packing on small molecule complexation and suggest that EUDOC is able to predict small-molecule complexes and that it is useful for the design of new materials, molecular sensors, and multimeric inhibitors of protein-protein interactions

Enhancing Deep Neural Networks Testing by Traversing Data Manifold

We develop DEEPTRAVERSAL, a feedback-driven framework to test DNNs. DEEPTRAVERSAL first launches an offline phase to map media data of various forms to manifolds. Then, in its online testing phase, DEEPTRAVERSAL traverses the prepared manifold space to maximize DNN coverage criteria and trigger prediction errors. In our evaluation, DNNs executing various tasks (e.g., classification, self-driving, machine translation) and media data of different types (image, audio, text) were used. DEEPTRAVERSAL exhibits better performance than prior methods with respect to popular DNN coverage criteria and it can discover a larger number and higher quality of error-triggering inputs. The tested DNN models, after being repaired with findings of DEEPTRAVERSAL, achieve better accurac

MDPFuzz: Testing Models Solving Markov Decision Processes

The Markov decision process (MDP) provides a mathematical framework for modeling sequential decision-making problems, many of which are crucial to security and safety, such as autonomous driving and robot control. The rapid development of artificial intelligence research has created efficient methods for solving MDPs, such as deep neural networks (DNNs), reinforcement learning (RL), and imitation learning (IL). However, these popular models for solving MDPs are neither thoroughly tested nor rigorously reliable. We present MDPFuzzer, the first blackbox fuzz testing framework for models solving MDPs. MDPFuzzer forms testing oracles by checking whether the target model enters abnormal and dangerous states. During fuzzing, MDPFuzzer decides which mutated state to retain by measuring if it can reduce cumulative rewards or form a new state sequence. We design efficient techniques to quantify the "freshness" of a state sequence using Gaussian mixture models (GMMs) and dynamic expectation-maximization (DynEM). We also prioritize states with high potential of revealing crashes by estimating the local sensitivity of target models over states. MDPFuzzer is evaluated on five state-of-the-art models for solving MDPs, including supervised DNN, RL, IL, and multi-agent RL. Our evaluation includes scenarios of autonomous driving, aircraft collision avoidance, and two games that are often used to benchmark RL. During a 12-hour run, we find over 80 crash-triggering state sequences on each model. We show inspiring findings that crash-triggering states, though look normal, induce distinct neuron activation patterns compared with normal states. We further develop an abnormal behavior detector to harden all the evaluated models and repair them with the findings of MDPFuzzer to significantly enhance their robustness without sacrificing accuracy

Estimating Individualized Decision Rules with Tail Controls

With the emergence of precision medicine, estimating optimal individualized decision rules (IDRs) has attracted tremendous attention in many scientific areas. Most existing literature has focused on finding optimal IDRs that can maximize the expected outcome for each individual. Motivated by complex individualized decision making procedures and popular conditional value at risk (CVaR) measures, we propose a new robust criterion to estimate optimal IDRs in order to control the average lower tail of the subjects' outcomes. In addition to improving the individualized expected outcome, our proposed criterion takes risks into consideration, and thus the resulting IDRs can prevent adverse events. The optimal IDR under our criterion can be interpreted as the decision rule that maximizes the ``worst-case" scenario of the individualized outcome when the underlying distribution is perturbed within a constrained set. An efficient non-convex optimization algorithm is proposed with convergence guarantees. We investigate theoretical properties for our estimated optimal IDRs under the proposed criterion such as consistency and finite sample error bounds. Simulation studies and a real data application are used to further demonstrate the robust performance of our method

4,4′-[8b,8c-Bis(ethoxycarbonyl)-4,8-dioxo-2,3,5,6-tetra­hydro-1H,4H-2,3a,4a,6,7a,8a-hexa­azacyclo­penta­[def]fluorene-2,6-diyl]dipyridinium bis­(tetra­fluorido­borate)

In the title compound, C26H32N8O6 2+·2BF4 −, the cation is built up from four fused rings, viz. two nearly planar imidazole rings and two triazine rings exhibiting chair conformations. One eth­oxy group is disordered between two positions in an approximate ratio 3:2. The F atoms of the two anions are each rotationally disordered between two orientations in the same 3:2 ratio. The crystal structure is stabilized by inter­molecular N—H⋯O, C—H⋯F and N—H⋯F inter­actions

Solving Einstein equations using deep learning

Einstein field equations are notoriously challenging to solve due to their complex mathematical form, with few analytical solutions available in the absence of highly symmetric systems or ideal matter distribution. However, accurate solutions are crucial, particularly in systems with strong gravitational field such as black holes or neutron stars. In this work, we use neural networks and auto differentiation to solve the Einstein field equations numerically inspired by the idea of physics-informed neural networks (PINNs). By utilizing these techniques, we successfully obtain the Schwarzschild metric and the charged Schwarzschild metric given the energy-momentum tensor of matter. This innovative method could open up a different way for solving space-time coupled Einstein field equations and become an integral part of numerical relativity.Comment: 18 pages, 4 figure