The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry

While the introduction of practical deep learning has driven progress across scientific fields, recent research highlighted that the requirement of deep learning for ever-increasing computational resources and data has potential negative impacts on the scientific community and society as a whole. An ever-growing need for more computational resources may exacerbate the concentration of funding, the exclusiveness of research, and thus the inequality between countries, sectors, and institutions. Here, I introduce recent concerns and considerations of the machine learning research community that could affect chemistry and present potential solutions, including more detailed assessments of model performance, increased adherence to open science and open data practices, an increase in multinational and multi-institutional collaboration, and a focus on thematic and cultural diversity

A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges

Reaction and retrosynthesis prediction are fundamental tasks in computational chemistry that have recently garnered attention from both the machine learning and drug discovery communities. Various deep learning approaches have been proposed to tackle these problems, and some have achieved initial success. In this survey, we conduct a comprehensive investigation of advanced deep learning-based models for reaction and retrosynthesis prediction. We summarize the design mechanisms, strengths, and weaknesses of state-of-the-art approaches. Then, we discuss the limitations of current solutions and open challenges in the problem itself. Finally, we present promising directions to facilitate future research. To our knowledge, this paper is the first comprehensive and systematic survey that seeks to provide a unified understanding of reaction and retrosynthesis prediction.Comment: Accepted as IJCAI 2023 Surve

CLBlast: A Tuned OpenCL BLAS Library

This work introduces CLBlast, an open-source BLAS library providing optimized OpenCL routines to accelerate dense linear algebra for a wide variety of devices. It is targeted at machine learning and HPC applications and thus provides a fast matrix-multiplication routine (GEMM) to accelerate the core of many applications (e.g. deep learning, iterative solvers, astrophysics, computational fluid dynamics, quantum chemistry). CLBlast has five main advantages over other OpenCL BLAS libraries: 1) it is optimized for and tested on a large variety of OpenCL devices including less commonly used devices such as embedded and low-power GPUs, 2) it can be explicitly tuned for specific problem-sizes on specific hardware platforms, 3) it can perform operations in half-precision floating-point FP16 saving bandwidth, time and energy, 4) it has an optional CUDA back-end, 5) and it can combine multiple operations in a single batched routine, accelerating smaller problems significantly. This paper describes the library and demonstrates the advantages of CLBlast experimentally for different use-cases on a wide variety of OpenCL hardware.Comment: Conference paper in: IWOCL '18, the International Workshop on OpenC

Continuous Perception for Immersive Interaction and Computation in Molecular Sciences

Chemistry aims to understand the structure and reactions of molecules, which involve phenomena occurring at microscopic scales. However, scientists perceive the world at macroscopic scales, making it difficult to study complex molecular objects. Graphical representations, such as structural formulas, were developed to bridge this gap and aid in understanding. The advent of Quantum Mechanics further increased the complexity of the representation of microscopic objects. This dichotomy between conceptual representation and predictive quantification forms the foundation of Chemistry, now further explored with the rise of Artificial Intelligence. Recent advancements in computational sciences, increased computational power, and developments in Machine-Learning (ML) raise questions about the traditional scientific method. Computational scientists, who have relied on approximations based on fundamental rules, now face the possibility of accurately simulating nature without strictly adhering to its laws. This shift challenges the association between progress in understanding a phenomenon and the ability to predict it. Deep learning models can not only make predictions but also create new data. While these techniques find applications in fields like Natural Language Processing, they suffer from limitations and lack true intelligence or awareness of physical laws. The thesis aims to create mathematical descriptors for atom types, bond types, and angle types in ML procedures, ensuring the retention of their chemical meaning. The goal is to make quantitative predictions while interpreting changes in descriptors as chemical changes. To achieve this, the thesis develops a software called Proxima for Molecular Perception, which automatically perceives features from molecules. Proxima treats strongly coupled electrons as covalent bonds and lone pairs, while delocalized electrons are modeled using a Tight-Binding model. The resulting Molecular Graph captures the weak interactions between these units. Overall, this thesis explores the intersection of computational chemistry and Machine-Learning to enhance our understanding and predictive capabilities in the field of Chemistry by building the so-called Virtual Laboratory, a virtual environment with automatic access to structural databases to test chemical ideas on the fly (pre-processing) and explore the output of computational software (post-processing).  &nbsp