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Abstract This review article discusses some common enhanced sampling methods in relation to the process of self-assembly of biomolecules. An introduction to self-assembly and its challenges is covered followed by a brief overview of the methods and analysis for replica-exchange molecular dynamics, umbrella sampling, metadynamics, and machine learning based techniques. Applications of select methods towards peptides, proteins, polymers, and nucleic acids are discussed. Finally, a short discussion of the future directions of some of these methods is provided. 

Soft materials are critical to the pharmaceutical industry for their role in formulations, delivery of active compounds or understanding relevant physiological processes. This chapter will focus on coarse-grained (CG) approaches and models that have been used in conjunction with the Molecular Dynamics (MD) simulation method to investigate soft materials of interest to various applications in pharmaceutical sciences. This chapter also discusses several examples of CG MD simulations used to scientifically probe molecules with different chemistries. 

Program sketching is a program synthesis paradigm in which the programmer provides a partial program with holes and assertions. The goal of the synthesizer is to automatically find integer values for the holes so that the resulting program satisfies the assertions. The most popular sketching tool, Sketch , can efficiently solve complex program sketches but uses an integer encoding that often performs poorly if the sketched program manipulates large integer values. In this article, we propose a new solving technique that allows Sketch to handle large integer values while retaining its integer encoding. Our technique uses a result from number theory, the Chinese Remainder Theorem, to rewrite program sketches to only track the remainders of certain variable values with respect to several prime numbers. We prove that our transformation is sound and the encoding of the resulting programs are exponentially more succinct than existing Sketch encodings. We evaluate our technique on a variety of benchmarks manipulating large integer values. Our technique provides speedups against both existing Sketch solvers and can solve benchmarks that existing Sketch solvers cannot handle. 

Incorporating symbolic reasoning into machine learning algorithms is a promising approach to improve performance on learning tasks that re- quire logical reasoning. We study the problem of generating a programmatic variant of referring expressions that we call referring relational pro- grams. In particular, given a symbolic representation of an image and a target object in that image, the goal is to generate a relational program that uniquely identifies the target object in terms of its attributes and its relations to other objects in the image. We propose a neurosymbolic program synthesis algorithm that combines a policy neural network with enumerative search to generate such relational programs. The policy neural net- work employs a program interpreter that provides immediate feedback on the consequences of the decisions made by the policy, and also takes into account the uncertainty in the symbolic representation of the image. We evaluate our algorithm on challenging benchmarks based on the CLEVR dataset, and demonstrate that our approach significantly outperforms several baselines.