This paper demonstrates that language models are strong structure-based protein designers. We present LM-Design, a generic approach to reprogramming sequence-based protein language models (pLMs), that have learned massive sequential evolutionary knowledge from the universe of natural protein sequences, to acquire an immediate capability to design preferable protein sequences for given folds. We conduct a structural surgery on pLMs, where a lightweight structural adapter is implanted into pLMs and endows it with structural awareness. During inference, iterative refinement is performed to effectively optimize the generated protein sequences. Experiments show that LM-Design improves the state-of-the-art results by a large margin, leading to 4% to 12% accuracy gains in sequence recovery (e.g., 55.65%/56.63% on CATH 4.2/4.3 single-chain benchmarks, and $>$60% when designing protein complexes). We provide extensive and in-depth analyses, which verify that LM-Design can (1) indeed leverage both structural and sequential knowledge to accurately handle structurally non-deterministic regions, (2) benefit from scaling data and model size, and (3) generalize to other proteins (e.g., antibodies and de novo proteins).

Drug design is a crucial step in the drug discovery cycle. Recently, various deep learning-based methods design drugs by generating novel molecules from scratch, avoiding traversing large-scale drug libraries. However, they depend on scarce experimental data or time-consuming docking simulation, leading to overfitting issues with limited training data and slow generation speed. In this study, we propose the zero-shot drug design method DESERT (Drug dEsign by SkEtching and geneRaTing). Specifically, DESERT splits the design process into two stages: sketching and generating, and bridges them with the molecular shape. The two-stage fashion enables our method to utilize the large-scale molecular database to reduce the need for experimental data and docking simulation. Experiments show that DESERT achieves a new state-of-the-art at a fast speed.

Predicting energetically favorable 3-dimensional conformations of organic molecules frommolecular graph plays a fundamental role in computer-aided drug discovery research.However, effectively exploring the high-dimensional conformation space to identify (meta) stable conformers is anything but trivial.In this work, we introduce RMCF, a novel framework to generate a diverse set of low-energy molecular conformations through samplingfrom a regularized molecular conformation field.We develop a data-driven molecular segmentation algorithm to automatically partition each molecule into several structural building blocks to reduce the modeling degrees of freedom.Then, we employ a Markov Random Field to learn the joint probability distribution of fragment configurations and inter-fragment dihedral angles, which enables us to sample from different low-energy regions of a conformation space.Our model constantly outperforms state-of-the-art models for the conformation generation task on the GEOM-Drugs dataset.We attribute the success of RMCF to modeling in a regularized feature space and learning a global fragment configuration distribution for effective sampling.The proposed method could be generalized to deal with larger biomolecular systems.

Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh, Kai-Wei Chang, Xuanjing Huang. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Deep neural networks are vulnerable to adversarial attacks, where a small perturbation to an input alters the model prediction.In many cases, malicious inputs intentionally crafted for one model can fool another model.In this paper, we present the first study to systematically investigate the transferability of adversarial examples for text classification models and explore how various factors, including network architecture, tokenization scheme, word embedding, and model capacity, affect the transferability of adversarial examples.Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models.Such adversarial examples reflect the defects of the learning process and the data bias in the training set.Finally, we derive word replacement rules that can be used for model diagnostics from these adversarial examples. A.2 Transferability among Different Neural ModelsWe show in Figure 4 the transferability rate among all neural models in the model pool.The column and row headers indicate the IDs of source and target models respectively.The mapping of IDs and the corresponding models is shown in Figure 8.We generate adversarial examples by attacking a source model, and report the transferability rates on a target (or victim) model. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 .71.50.68.66.21.30.50.39.48.48.22.27.51.49.57.50.21.29 .44 .38 2 .55.63.58.59.21.33 .46.47.46.48.20 .31.49.59.52 .50.18.30.45.47 3 .68.53.70.70.22.28.49.41.50.48.21.26.52 .50.58.55.20 .27.44 .41 4 .

Recently, many efforts have been devoted to generating responses expressing a specific emotion or relating to a given topic in a controlled manner. However, limited attention has been given to generating responses with a specified syntactic pattern, which makes it possible to imitate someone's way of speaking in dialogue. To fulfill this goal, we propose two models to generate syntax-aware responses: a gross-constraint and a specific-constraint model. The former controls the syntactic patterns of generated responses at sentence-level, while the latter works at smaller language units, such as words or phrases, being capable of manipulating the syntactic structures of responses in a more subtle manner. The extensive experimental results on two different datasets show that both the two models not only can generate meaningful responses with a specific and coherent structure but also improve on the diversity of generated responses, with similar gains in readability, relevance, and diversity as measured by human judges.

Despite achieving prominent performance on many important tasks, it has been reported that neural networks are vulnerable to adversarial examples.Previously studies along this line mainly focused on semantic tasks such as sentiment analysis, question answering and reading comprehension.In this study, we show that adversarial examples also exist in dependency parsing: we propose two approaches to study where and how parsers make mistakes by searching over perturbations to existing texts at sentence and phrase levels, and design algorithms to construct such examples in both of the black-box and white-box settings.Our experiments with one of state-of-the-art parsers on the English Penn Treebank (PTB) show that up to 77% of input examples admit adversarial perturbations, and we also show that the robustness of parsing models can be improved by crafting high-quality adversaries and including them in the training stage, while suffering little to no performance drop on the clean input data.

Recurrent neural networks (RNN) combined with attention mechanism has proved to be useful for various NLP tasks including machine translation, sequence labeling and syntactic parsing. The attention mechanism is usually applied by estimating the weights (or importance) of inputs and taking the weighted sum of inputs as derived features. Although such features have demonstrated their effectiveness, they may fail to capture the sequence information due to the simple weighted sum being used to produce them. The order of the words does matter to the meaning or the structure of the sentences, especially for syntactic parsing, which aims to recover the structure from a sequence of words. In this study, we propose an RNN-based attention to capture the relevant and sequence-preserved features from a sentence, and use the derived features to perform the dependency parsing. We evaluated the graph-based and transition-based parsing models enhanced with the RNN-based sequence-preserved attention on the both English PTB and Chinese CTB datasets. The experimental results show that the enhanced systems were improved with significant increase in parsing accuracy.