Intelligent System for Automated Molecular Patent Infringement Assessment

Automated drug discovery offers significant potential for accelerating the development of novel therapeutics by substituting labor-intensive human workflows with machine-driven processes. However, a critical bottleneck persists in the inability of current automated frameworks to assess whether newly designed molecules infringe upon existing patents, posing significant legal and financial risks. We introduce PatentFinder, a novel tool-enhanced and multi-agent framework that accurately and comprehensively evaluates small molecules for patent infringement. It incorporates both heuristic and model-based tools tailored for decomposed subtasks, featuring: MarkushParser, which is capable of optical chemical structure recognition of molecular and Markush structures, and MarkushMatcher, which enhances large language models' ability to extract substituent groups from molecules accurately. On our benchmark dataset MolPatent-240, PatentFinder outperforms baseline approaches that rely solely on large language models, demonstrating a 13.8\% increase in F1-score and a 12\% rise in accuracy. Experimental results demonstrate that PatentFinder mitigates label bias to produce balanced predictions and autonomously generates detailed, interpretable patent infringement reports. This work not only addresses a pivotal challenge in automated drug discovery but also demonstrates the potential of decomposing complex scientific tasks into manageable subtasks for specialized, tool-augmented agents.

MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild

In recent decades, chemistry publications and patents have increased rapidly. A significant portion of key information is embedded in molecular structure figures, complicating large-scale literature searches and limiting the application of large language models in fields such as biology, chemistry, and pharmaceuticals. The automatic extraction of precise chemical structures is of critical importance. However, the presence of numerous Markush structures in real-world documents, along with variations in molecular image quality, drawing styles, and noise, significantly limits the performance of existing optical chemical structure recognition (OCSR) methods. We present MolParser, a novel end-to-end OCSR method that efficiently and accurately recognizes chemical structures from real-world documents, including difficult Markush structure. We use a extended SMILES encoding rule to annotate our training dataset. Under this rule, we build MolParser-7M, the largest annotated molecular image dataset to our knowledge. While utilizing a large amount of synthetic data, we employed active learning methods to incorporate substantial in-the-wild data, specifically samples cropped from real patents and scientific literature, into the training process. We trained an end-to-end molecular image captioning model, MolParser, using a curriculum learning approach. MolParser significantly outperforms classical and learning-based methods across most scenarios, with potential for broader downstream applications. The dataset is publicly available.

A high-accuracy multi-model mixing retrosynthetic method

The field of computer-aided synthesis planning (CASP) has seen rapid advancements in recent years, achieving significant progress across various algorithmic benchmarks. However, chemists often encounter numerous infeasible reactions when using CASP in practice. This article delves into common errors associated with CASP and introduces a product prediction model aimed at enhancing the accuracy of single-step models. While the product prediction model reduces the number of single-step reactions, it integrates multiple single-step models to maintain the overall reaction count and increase reaction diversity. Based on manual analysis and large-scale testing, the product prediction model, combined with the multi-model ensemble approach, has been proven to offer higher feasibility and greater diversity.

SciLitLLM: How to Adapt LLMs for Scientific Literature Understanding

Scientific literature understanding is crucial for extracting targeted information and garnering insights, thereby significantly advancing scientific discovery. Despite the remarkable success of Large Language Models (LLMs), they face challenges in scientific literature understanding, primarily due to (1) a lack of scientific knowledge and (2) unfamiliarity with specialized scientific tasks. To develop an LLM specialized in scientific literature understanding, we propose a hybrid strategy that integrates continual pre-training (CPT) and supervised fine-tuning (SFT), to simultaneously infuse scientific domain knowledge and enhance instruction-following capabilities for domain-specific tasks.cIn this process, we identify two key challenges: (1) constructing high-quality CPT corpora, and (2) generating diverse SFT instructions. We address these challenges through a meticulous pipeline, including PDF text extraction, parsing content error correction, quality filtering, and synthetic instruction creation. Applying this strategy, we present a suite of LLMs: SciLitLLM, specialized in scientific literature understanding. These models demonstrate promising performance on scientific literature understanding benchmarks. Our contributions are threefold: (1) We present an effective framework that integrates CPT and SFT to adapt LLMs to scientific literature understanding, which can also be easily adapted to other domains. (2) We propose an LLM-based synthesis method to generate diverse and high-quality scientific instructions, resulting in a new instruction set -- SciLitIns -- for supervised fine-tuning in less-represented scientific domains. (3) SciLitLLM achieves promising performance improvements on scientific literature understanding benchmarks.

Uni-Mol2: Exploring Molecular Pretraining Model at Scale

In recent years, pretraining models have made significant advancements in the fields of natural language processing (NLP), computer vision (CV), and life sciences. The significant advancements in NLP and CV are predominantly driven by the expansion of model parameters and data size, a phenomenon now recognized as the scaling laws. However, research exploring scaling law in molecular pretraining models remains unexplored. In this work, we present Uni-Mol2 , an innovative molecular pretraining model that leverages a two-track transformer to effectively integrate features at the atomic level, graph level, and geometry structure level. Along with this, we systematically investigate the scaling law within molecular pretraining models, characterizing the power-law correlations between validation loss and model size, dataset size, and computational resources. Consequently, we successfully scale Uni-Mol2 to 1.1 billion parameters through pretraining on 800 million conformations, making it the largest molecular pretraining model to date. Extensive experiments show consistent improvement in the downstream tasks as the model size grows. The Uni-Mol2 with 1.1B parameters also outperforms existing methods, achieving an average 27% improvement on the QM9 and 14% on COMPAS-1D dataset.

Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction

In recent years, machine learning (ML) methods have emerged as promising alternatives for molecular docking, offering the potential for high accuracy without incurring prohibitive computational costs. However, recent studies have indicated that these ML models may overfit to quantitative metrics while neglecting the physical constraints inherent in the problem. In this work, we present Uni-Mol Docking V2, which demonstrates a remarkable improvement in performance, accurately predicting the binding poses of 77+% of ligands in the PoseBusters benchmark with an RMSD value of less than 2.0 {\AA}, and 75+% passing all quality checks. This represents a significant increase from the 62% achieved by the previous Uni-Mol Docking model. Notably, our Uni-Mol Docking approach generates chemically accurate predictions, circumventing issues such as chirality inversions and steric clashes that have plagued previous ML models. Furthermore, we observe enhanced performance in terms of high-quality predictions (RMSD values of less than 1.0 {\AA} and 1.5 {\AA}) and physical soundness when Uni-Mol Docking is combined with more physics-based methods like Uni-Dock. Our results represent a significant advancement in the application of artificial intelligence for scientific research, adopting a holistic approach to ligand docking that is well-suited for industrial applications in virtual screening and drug design. The code, data and service for Uni-Mol Docking are publicly available for use and further development in https://github.com/dptech-corp/Uni-Mol.

Uni-SMART: Universal Science Multimodal Analysis and Research Transformer

In scientific research and its application, scientific literature analysis is crucial as it allows researchers to build on the work of others. However, the fast growth of scientific knowledge has led to a massive increase in scholarly articles, making in-depth literature analysis increasingly challenging and time-consuming. The emergence of Large Language Models (LLMs) has offered a new way to address this challenge. Known for their strong abilities in summarizing texts, LLMs are seen as a potential tool to improve the analysis of scientific literature. However, existing LLMs have their own limits. Scientific literature often includes a wide range of multimodal elements, such as molecular structure, tables, and charts, which are hard for text-focused LLMs to understand and analyze. This issue points to the urgent need for new solutions that can fully understand and analyze multimodal content in scientific literature. To answer this demand, we present Uni-SMART (Universal Science Multimodal Analysis and Research Transformer), an innovative model designed for in-depth understanding of multimodal scientific literature. Through rigorous quantitative evaluation across several domains, Uni-SMART demonstrates superior performance over leading text-focused LLMs. Furthermore, our exploration extends to practical applications, including patent infringement detection and nuanced analysis of charts. These applications not only highlight Uni-SMART's adaptability but also its potential to revolutionize how we interact with scientific literature.

SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis

Recent breakthroughs in Large Language Models (LLMs) have revolutionized natural language understanding and generation, igniting a surge of interest in leveraging these technologies in the field of scientific literature analysis. Existing benchmarks, however, inadequately evaluate the proficiency of LLMs in scientific literature analysis, especially in scenarios involving complex comprehension and multimodal data. In response, we introduced SciAssess, a benchmark tailored for the in-depth analysis of scientific literature, crafted to provide a thorough assessment of LLMs' efficacy. SciAssess focuses on evaluating LLMs' abilities in memorization, comprehension, and analysis within the context of scientific literature analysis. It includes representative tasks from diverse scientific fields, such as general chemistry, organic materials, and alloy materials. And rigorous quality control measures ensure its reliability in terms of correctness, anonymization, and copyright compliance. SciAssess evaluates leading LLMs, including GPT-4, GPT-3.5, and Gemini, identifying their strengths and aspects for improvement and supporting the ongoing development of LLM applications in scientific literature analysis. SciAssess and its resources are made available at https://sci-assess.github.io, offering a valuable tool for advancing LLM capabilities in scientific literature analysis.

End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction

Powder X-ray diffraction (PXRD) is a crucial means for crystal structure determination. Such determination often involves external database matching to find a structural analogue and Rietveld refinement to obtain finer structure. However, databases may be incomplete and Rietveld refinement often requires intensive trial-and-error efforts from trained experimentalists, which remains ineffective in practice. To settle these issues, we propose XtalNet, the first end-to-end deep learning-based framework capable of ab initio generation of crystal structures that accurately match given PXRD patterns. The model employs contrastive learning and Diffusion-based conditional generation to enable the simultaneous execution of two tasks: crystal structure retrieval based on PXRD patterns and conditional structure generations. To validate the effectiveness of XtalNet, we curate a much more challenging and practical dataset hMOF-100, XtalNet performs well on this dataset, reaching 96.3\% top-10 hit ratio on the database retrieval task and 95.0\% top-10 match rate on the ranked structure generation task.

Node-Aligned Graph-to-Graph Generation for Retrosynthesis Prediction

Single-step retrosynthesis is a crucial task in organic chemistry and drug design, requiring the identification of required reactants to synthesize a specific compound. with the advent of computer-aided synthesis planning, there is growing interest in using machine-learning techniques to facilitate the process. Existing template-free machine learning-based models typically utilize transformer structures and represent molecules as ID sequences. However, these methods often face challenges in fully leveraging the extensive topological information of the molecule and aligning atoms between the production and reactants, leading to results that are not as competitive as those of semi-template models. Our proposed method, Node-Aligned Graph-to-Graph (NAG2G), also serves as a transformer-based template-free model but utilizes 2D molecular graphs and 3D conformation information. Furthermore, our approach simplifies the incorporation of production-reactant atom mapping alignment by leveraging node alignment to determine a specific order for node generation and generating molecular graphs in an auto-regressive manner node-by-node. This method ensures that the node generation order coincides with the node order in the input graph, overcoming the difficulty of determining a specific node generation order in an auto-regressive manner. Our extensive benchmarking results demonstrate that the proposed NAG2G can outperform the previous state-of-the-art baselines in various metrics.