Qiming Sun, Timothy C. Berkelbach, Nick S. Blunt, George H. Booth, Sheng Guo, Zhendong Li, Junzi Liu, James McClain, Elvira R. Sayfutyarova, Sandeep Sharma, Sebastian Wouters, Garnet Kin-Lic Chan <<<

PySCF is a general-purpose electronic structure platform designed from theground up to emphasize code simplicity, both to aid new method development, aswell as for flexibility in computational workflow. The package provides a widerange of tools to support simulations of finite size systems, extended systemswith periodic boundary conditions, low dimensional periodic systems, and customHamiltonians, using mean-field and post-mean-field methods with standardGaussian basis functions. To ensure easy of extensibility, PySCF uses thePython language to implement almost all its features, while computationallycritical paths are implemented with heavily optimized C routines. Using thiscombined Python/C implementation, the package is as efficient as the bestexisting C or Fortran based quantum chemistry programs. In this paper wedocument the capabilities and design philosophy of the current version of thePySCF package. <<<

Recent advancements in quantum computing have opened new avenues for tacklinglong-standing complex combinatorial optimization problems that are intractablefor classical computers. Predicting secondary structure of mRNA is one suchnotoriously difficult problem that can benefit from the ever-increasingmaturity of quantum computing technology. Accurate prediction of mRNA secondarystructure is critical in designing RNA-based therapeutics as it dictatesvarious steps of an mRNA life cycle, including transcription, translation, anddecay. The current generation of quantum computers have reached utility-scale,allowing us to explore relatively large problem sizes. In this paper, weexamine the feasibility of solving mRNA secondary structures on a quantumcomputer with sequence length up to 60 nucleotides representing problems in thequbit range of 10 to 80. We use Conditional Value at Risk (CVaR)-based VQEalgorithm to solve the optimization problems, originating from the mRNAstructure prediction problem, on the IBM Eagle and Heron quantum processors. Toour encouragement, even with ``minimal'' error mitigation and fixed-depthcircuits, our hardware runs yield accurate predictions of minimum free energy(MFE) structures that match the results of the classical solver CPLEX. Ourresults provide sufficient evidence for the viability of solving mRNA structureprediction problems on a quantum computer and motivate continued research inthis direction. <<<

Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W. R. Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis <<<

AbstractIn this article, we will introduce the basic concept and the quantum feature of a novel computing system, coherent Ising machines, and describe their theoretical and experimental performance. We start with the discussion how to construct such physical devices as the quantum analog of classical neuron and synapse, and end with the performance comparison against various classical neural networks implemented in CPU and supercomputers. <<<

The conformational landscape of proteins is crucial to understanding theirfunctionality in complex biological processes. Traditional physics-basedcomputational methods, such as molecular dynamics (MD) simulations, suffer fromrare event sampling and long equilibration time problems, hindering theirapplications in general protein systems. Recently, deep generative modelingtechniques, especially diffusion models, have been employed to generate novelprotein conformations. However, existing score-based diffusion methods cannotproperly incorporate important physical prior knowledge to guide the generationprocess, causing large deviations in the sampled protein conformations from theequilibrium distribution. In this paper, to overcome these limitations, wepropose a force-guided SE(3) diffusion model, ConfDiff, for proteinconformation generation. By incorporating a force-guided network with a mixtureof data-based score models, ConfDiff can can generate protein conformationswith rich diversity while preserving high fidelity. Experiments on a variety ofprotein conformation prediction tasks, including 12 fast-folding proteins andthe Bovine Pancreatic Trypsin Inhibitor (BPTI), demonstrate that our methodsurpasses the state-of-the-art method. <<<

There is a growing body of work that leverages features extracted viatopological data analysis to train machine learning models. While this field,sometimes known as topological machine learning (TML), has seen some notablesuccesses, an understanding of how the process of learning from topologicalfeatures differs from the process of learning from raw data is still limited.In this work, we begin to address one component of this larger issue by askingwhether a model trained with topological features learns internalrepresentations of data that are fundamentally different than those learned bya model trained with the original raw data. To quantify ``different'', weexploit two popular metrics that can be used to measure the similarity of thehidden representations of data within neural networks, neural stitching andcentered kernel alignment. From these we draw a range of conclusions about howtraining with topological features does and does not change the representationsthat a model learns. Perhaps unsurprisingly, we find that structurally, thehidden representations of models trained and evaluated on topological featuresdiffer substantially compared to those trained and evaluated on thecorresponding raw data. On the other hand, our experiments show that in somecases, these representations can be reconciled (at least to the degree requiredto solve the corresponding task) using a simple affine transformation. Weconjecture that this means that neural networks trained on raw data may extractsome limited topological features in the process of making predictions. <<<

Topological Data Analysis is a recent and fast growing field providing a setof new topological and geometric tools to infer relevant features for possiblycomplex data. This paper is a brief introduction, through a few selectedtopics, to basic fundamental and practical aspects of \tda\ for non experts. <<<

Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, Ross Girshick <<<

We introduce the Segment Anything (SA) project: a new task, model, anddataset for image segmentation. Using our efficient model in a data collectionloop, we built the largest segmentation dataset to date (by far), with over 1billion masks on 11M licensed and privacy respecting images. The model isdesigned and trained to be promptable, so it can transfer zero-shot to newimage distributions and tasks. We evaluate its capabilities on numerous tasksand find that its zero-shot performance is impressive -- often competitive withor even superior to prior fully supervised results. We are releasing theSegment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and11M images at https://segment-anything.com to foster research into foundationmodels for computer vision. <<<

Diffusion models have significantly advanced the fields of image, audio, andvideo generation, but they depend on an iterative sampling process that causesslow generation. To overcome this limitation, we propose consistency models, anew family of models that generate high quality samples by directly mappingnoise to data. They support fast one-step generation by design, while stillallowing multistep sampling to trade compute for sample quality. They alsosupport zero-shot data editing, such as image inpainting, colorization, andsuper-resolution, without requiring explicit training on these tasks.Consistency models can be trained either by distilling pre-trained diffusionmodels, or as standalone generative models altogether. Through extensiveexperiments, we demonstrate that they outperform existing distillationtechniques for diffusion models in one- and few-step sampling, achieving thenew state-of-the-art FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64x64 forone-step generation. When trained in isolation, consistency models become a newfamily of generative models that can outperform existing one-step,non-adversarial generative models on standard benchmarks such as CIFAR-10,ImageNet 64x64 and LSUN 256x256. <<<

The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of proteins. Along with novel architectures for generative modeling and sequence analysis, they have revolutionized the protein design field in the past few years remarkably by improving the accuracy and ability to identify novel protein sequences and structures. Deep neural networks can now learn and extract the fundamental features of protein structures, predict how they interact with other biomolecules, and have the potential to create new effective drugs for treating disease. As their applicability in protein design is rapidly growing, we review the recent developments and technology in deep learning methods and provide examples of their performance to generate novel functional proteins. <<<

Qiming Sun, Xing Zhang, Samragni Banerjee, Peng Bao, Marc Barbry, Nick S Blunt, Nikolay A Bogdanov, George H Booth, Jia Chen, Zhi-Hao Cui, Janus J Eriksen, Yang Gao, Sheng Guo, Jan Hermann, Matthew R Hermes, Kevin Koh, Peter Koval, Susi Lehtola, Zhendong Li, Junzi Liu, Narbe Mardirossian, James D McClain, Mario Motta, Bastien Mussard, Hung Q Pham, Artem Pulkin, Wirawan Purwanto, Paul J Robinson, Enrico Ronca, Elvira R Sayfutyarova, Maximilian Scheurer, Henry F Schurkus, James E T Smith, Chong Sun, Shi-Ning Sun, Shiv Upadhyay, Lucas K Wagner, Xiao Wang, Alec White, James Daniel Whitfield, Mark J Williamson, Sebastian Wouters, Jun Yang, Jason M Yu, Tianyu Zhu, Timothy C Berkelbach, Sandeep Sharma, Alexander Yu Sokolov, Garnet Kin-Lic Chan <<<

PySCF is a Python-based general-purpose electronic structure platform that supports first-principles simulations of molecules and solids as well as accelerates the development of new methodology and complex computational workflows. This paper explains the design and philosophy behind PySCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PySCF as a development environment. We then summarize the capabilities of PySCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PySCF across the domains of quantum chemistry, materials science, machine learning, and quantum information science. <<<

Recent events have pushed RNA research into the spotlight. Continued discoveries of RNA with unexpected diverse functions in healthy and diseased cells, such as the role of RNA as both the source and countermeasure to a severe acute respiratory syndrome coronavirus 2 infection, are igniting a new passion for understanding this functionally and structurally versatile molecule. Although RNA structure is key to function, many foundational characteristics of RNA structure are misunderstood, and the default state of RNA is often thought of and depicted as a single floppy strand. The purpose of this perspective is to help adjust mental models, equipping the community to better use the fundamental aspects of RNA structural information in new mechanistic models, enhance experimental design to test these models, and refine data interpretation. We discuss six core observations focused on the inherent nature of RNA structure and how to incorporate these characteristics to better understand RNA structure. We also offer some ideas for future efforts to make validated RNA structural information available and readily used by all researchers. <<<

Deep convolutional networks have demonstrated the state-of-the-art performance on various medical image computing tasks. Leveraging images from different modalities for the same analysis task holds clinical benefits. However, the generalization capability of deep models on test data with different distributions remain as a major challenge. In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e.g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner. Specifically, a domain adaptation module flexibly replaces the early encoder layers of the source network, and the higher layers are shared between domains. With adversarial learning, we build two discriminators whose inputs are respectively multi-level features and predicted segmentation masks. We have validated our domain adaptation method on cardiac structure segmentation in unpaired MRI and CT. The experimental results with comprehensive ablation studies demonstrate the excellent efficacy of our proposed PnP-AdaNet. Moreover, we introduce a novel benchmark on the cardiac dataset for the task of unsupervised cross-modality domain adaptation. We will make our code and database publicly available, aiming to promote future studies on this challenging yet important research topic in medical imaging. <<<

Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets. In this paper, we formulate SBDD as a 3D-conditional generation problem and present DiffSBDD, an SE(3)-equivariant 3D-conditional diffusion model that generates novel ligands conditioned on protein pockets. Comprehensive in silico experiments demonstrate the efficiency and effectiveness of DiffSBDD in generating novel and diverse drug-like ligands with competitive docking scores. We further explore the flexibility of the diffusion framework for a broader range of tasks in drug design campaigns, such as off-the-shelf property optimization and partial molecular design with inpainting. <<<

Prodrugs are one of the most common strategies for the design of targeted anticancer agents. However, their application is often hampered by the modifiable groups available on parent drugs. Herein, a carbon-carbon (C−C) bond cleavage-based prodrug activation strategy is reported, which was successfully used to design prodrugs of β-lapachone (β-lap), an ortho-quinone natural product without traditional modifiable groups for the construction of C−N/C−O bond cleavage-based prodrugs. The designed β-lap prodrug with a reactive oxygen species-specific trigger was quickly activated, releasing β-lap. It exerted anticancer efficacy via NAD(P)H:quinone oxidoreductase 1 (NQO1)-mediated futile redox cycling, resulting in potent cytotoxicity that was highly selective for NQO1-rich cancer cells over normal cells both in vitro and in vivo. This significantly amplified the therapeutic window of β-lap. This study provides a practical strategy for the design of prodrugs for parent drugs that do not contain traditional modifiable groups. <<<

Molecular design strategies are integral to therapeutic progress in drug discovery. Computational approaches for de novo molecular design have been developed over the past three decades and, recently, thanks in part to advances in machine learning (ML) and artificial intelligence (AI), the drug discovery field has gained practical experience. Here, we review these learnings and present de novo approaches according to the coarseness of their molecular representation: that is, whether molecular design is modeled on an atom-based, fragment-based, or reaction-based paradigm. Furthermore, we emphasize the value of strong benchmarks, describe the main challenges to using these methods in practice, and provide a viewpoint on further opportunities for exploration and challenges to be tackled in the upcoming years. <<<

Joseph L. Watson , David Juergens , Nathaniel R. Bennett , Brian L. Trippe , Jason Yim , Helen E. Eisenach , Woody Ahern , Andrew J. Borst , Robert J. Ragotte , Lukas F. Milles , Basile I. M. Wicky , Nikita Hanikel , Samuel J. Pellock , Alexis Courbet , William Sheffler , Jue Wang , Preetham Venkatesh , Isaac Sappington , Susana Vázquez Torres , Anna Lauko , Valentin De Bortoli , Emile Mathieu , Regina Barzilay , Tommi S. Jaakkola , Frank DiMaio , Minkyung Baek , David Baker <<<

AbstractThere has been considerable recent progress in designing new proteins using deep learning methods1–9. Despite this progress, a general deep learning framework for protein design that enables solution of a wide range of design challenges, includingde novobinder design and design of higher order symmetric architectures, has yet to be described. Diffusion models10,11have had considerable success in image and language generative modeling but limited success when applied to protein modeling, likely due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding, and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold Diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of new designs. In a manner analogous to networks which produce images from user-specified inputs, RFdiffusionenables the design of diverse, complex, functional proteins from simple molecular specifications. <<<