Neural Information Processing Systems http://nips.cc/

Antibiotic resistance presents a growing global health crisis, demanding new therapeutic strategies that target novel bacterial mechanisms. Recent advances in protein structure prediction and machine learning-driven molecule generation offer a promising opportunity to accelerate drug discovery. However, practical guidance on selecting and integrating these models into real-world pipelines remains limited. In this study, we develop an end-to-end, artificial intelligence-guided antibiotic discovery pipeline that spans target identification to compound realization. We leverage structure-based clustering across predicted proteomes of multiple pathogens to identify conserved, essential, and non-human-homologous targets. We then systematically evaluate six leading 3D-structure-aware generative models$\unicode{x2014}$spanning diffusion, autoregressive, graph neural network, and language model architectures$\unicode{x2014}$on their usability, chemical validity, and biological relevance. Rigorous post-processing filters and commercial analogue searches reduce over 100 000 generated compounds to a focused, synthesizable set. Our results highlight DeepBlock and TamGen as top performers across diverse criteria, while also revealing critical trade-offs between model complexity, usability, and output quality. This work provides a comparative benchmark and blueprint for deploying artificial intelligence in early-stage antibiotic development.

For five years or so, I have been running around as a pale imitation of Paul Revere, yelling, "The robots are coming! At schools, social settings, with family and friends, or even to complete strangers with whom I fell into conversations, I have uttered the same warning: "It's critical that you or your children identify a career -- now -- that won't be taken over by robots and artificial intelligence." My particular midnight ride started well before the pandemic reared its ugly head. But the pandemic may have planted a seed in the minds of certain CEOs that human beings are the weakest link on their chain to profit and prosperity. When the first "Terminator" movie was released -- eerily enough, in 1984 -- the world was introduced to Cyberdyne Systems and its "Skynet" artificial superintelligence system, which not only gained self-awareness but realized it could do everything infinitely faster and better than its human creators. Well, ever since that movie got people asking, "What if," the fictional theme -- and warnings about AI -- have been morphing into reality. The latest example of a technology poised to replace a human workforce is ChatGPT, the chatbot auto-generative system created by Open AI for online customer care. It is a pre-trained generative chat, which makes use of natural language processing, or NLP. The source of its data is textbooks, websites and various articles, which it uses to model its own language for responding to human interaction. It's certainly not a stretch to believe that any number of CEOs might think, "Interesting… A self-teaching artificial intelligence system that won't call in sick, doesn't need to be fed or to take bathroom breaks, does not require health care, but can and will work 24/7/365." Not shockingly, it has been reported that Microsoft, which is laying off 10,000 people, announced a "multiyear, multibillion-dollar investment" in this revolutionary technology, which apparently is growing smarter by the day. Pengcheng Shi, an associate dean in the Department of Computing and Information Sciences at Rochester Institute of Technology, warned in an interview with the New York Post: "AI is replacing the white-collar workers.

API development can be a time-consuming and complex task, but it doesn't have to be. With the advancements in natural language processing and machine learning, we now have access to tools like ChatGPT that can greatly simplify the process. In this blog post, we'll be taking a step-by-step approach to using ChatGPT to generate a Flask REST API. We'll cover everything from setting up…

Let's understand NLP and get all fundamental skills from SCRATCH! In this course you are invited to learn all the fundamental skills ... In this course you are invited to learn all the fundamental skills required in any kind of activity related to the Natural Language Processing and you will learn them from a theoretical and practical point of view, in fact you will seat together with me coding and implementing any topic step-by-step, instruction after instruction. Any of these projects will be a real and working use case so you will be able to re-use them in your own apps. In few words, this course is a real journey inside Natural Language Processing starting from the very beginning and finishing with the idea that all modern systems are leveraging: word embeddings. We are exploring NLU, NLG, NLP History, applications and use cases, studing Tokenization, Stopwords, Stemming, Lemmatization, PoS, NER, BoW, TF-IDF and Embeddings.

Code repository for this post is here: you can see the MLFlow Recipes template in the main branch and the filled-in template on the fill-in-steps branch. The announcement of MLFlow 2.0 included a new framework called MLFlow Recipes. For a Data Scientists, using MLFlow Recipes means cloning a git repository, or "template", that comes with a ready-to-go folder structure for any regression or binary classification problem. This folder structure includes everything, from library requirements, configuration, notebooks and tests, that's needed to make a data science project reproducible and production-ready. It's easy to start a new project with MLFlow Recipes -- git clone a template from the MLFlow repository, and you are good to go.

Sparse datasets are a common problem in machine learning, where many examples have a large number of missing or zero-valued features. This can lead to poor model performance and reduced interpretability of the results. In this article, we will provide a step-by-step guide on how to address the sparsity challenge in datasets, with a focus on real-world application. The first step in resolving the sparsity challenge is to understand why your dataset is sparse in the first place. Sparsity can be caused by the presence of irrelevant features, missing data, or categorical variables with a large number of levels.

How soon do you need to prepare for artificial intelligence? Artificial intelligence is already here – it's no longer a futuristic promise. And it's been here for years. Companies should already be thinking about how they can automate many of their ordinary marketing processes. This is the basic step that every company should take to make themselves more efficient.

This timely text/reference describes the development and implementation of large-scale distributed processing systems using open source tools and technologies such as Hadoop, Scalding and Spark. Comprehensive in scope, the book presents state-of-the-art material on building high performance distributed computing systems, providing practical guidance and best practices as well as describing theoretical software frameworks. Fulfilling the need for both introductory material for undergraduate students of computer science and detailed discussions for software engineering professionals, this book will aid a broad audience to understand the esoteric aspects of practical high performance computing through its use of solved problems, research case studies and working source code. Srinivasa is Professor and Head of the Department of Computer Science and Engineering at M.S. Ramaiah Institute of Technology (MSRIT), Bangalore, India. His other publications include the Springer title Soft Computing for Data Mining Applications.

Deep learning is the new big trend in machine learning. It had many recent successes in computer vision, automatic speech recognition and natural language processing. The goal of this blog post is to give you a hands-on introduction to deep learning. To do this, we will build a Cat/Dog image classifier using a deep learning algorithm called convolutional neural network (CNN) and a Kaggle dataset. This post is divided into 2 main parts. The first part covers some core concepts behind deep learning, while the second part is structured in a hands-on tutorial format. In the first part of the hands-on tutorial (section 4), we will build a Cat/Dog image classifier using a convolutional neural network from scratch.