In the world of drug discovery, choosing the right software to power innovation and streamline workflows is crucial for success. The landscape is experiencing a massive growth period and NVIDIA predicts that in 2025, the industry will be revolutionized by "drug discovery and design AI factories" that combine the latest advances of generative AI with robotics to eliminate much of the traditional trial-and-error approach.

This transformation is already visible as techbio and biopharma companies push the boundaries of AI integration, exploring near-infinite possible target drug combinations before conducting wet lab experiments. With rapid advancement, selecting the right software platform becomes critical. In 2025, organizations should evaluate solutions based on five key factors:

• Automation and AI Capabilities: Platforms leveraging AutoML and generative AI can significantly speed up workflows and offer predictive insights, allowing teams to focus on high-impact areas
• Specialized Modeling Techniques: Tools with advanced physics-based models and unique simulation capabilities, such as Free Energy Perturbation (FEP), provide crucial advantages in understanding complex molecular interactions
• User Accessibility and Customization: The balance between user-friendly design and customization potential significantly impacts adoption and workflow efficiency
• Cost and Licensing Options: Pricing structures, whether free, subscription-based, or API-driven, influence accessibility for organizations of all sizes
• Data Handling and Visualization: Efficient data management and clear visualization tools are essential for rapid decision-making and trend analysis

Based on these criteria, we evaluated seven software solutions that will define successful drug discovery in 2025. These platforms are expected to deliver more powerful capabilities in drug discovery such as accurate predictive analytics and streamlined automated processes. We’re hoping to see more growth in early-stage drug development through improved predictions of drug efficacy and side effects, while enabling de novo design of novel molecular structures. The integration of multi-omics data across genomics, proteomics, and metabolomics will help shift towards more comprehensive predictive models, as we start to see more platforms aim to streamline development processes from data management through to regulatory filings.

Chemical Computing Group: Comprehensive Molecular Modeling

Chemical Computing Group’s flagship software, MOE (Molecular Operating Environment), offers an all-in-one platform for drug discovery, integrating molecular modeling, cheminformatics, and bioinformatics. MOE excels in structure-based drug design, molecular docking, and QSAR modeling, supporting critical tasks like ADMET prediction and protein engineering. Its user-friendly interface and interactive 3D visualization tools make it accessible for a wide range of researchers. With modular workflows, machine learning integration, and flexible licensing options, MOE is a go-to solution for organizations of all sizes. As the platform continues to evolve with AI-driven innovations and expanded multi-omics support, it remains a key player in shaping the future of drug discovery.

deepmirror: Augmented Hit-to-Lead Optimization

deepmirror's platform aims to accelerate the drug discovery process, particularly in the hit-to-lead and lead optimization phases with deep generative AI. The platform is estimated to speed up the drug discovery process by up to six times in real world scenarios and has been demonstrated to help reduce ADMET liabilities in an antimalarial drug program. The platform's generative AI Engine utilizes foundational models that automatically adapt to user data to generate high quality molecules and achieve high performance on many molecular property prediction tasks. Furthermore, the platform also supports the prediction of protein-drug binding complexes with generative AI. deepmirror's user-friendly interface enables medicinal chemists to get their hands on advanced algorithms, empowering users to make more informed decisions regarding potency, selectivity, and other critical endpoints. It’s also ISO 27001 certified, ensuring data security and protection of intellectual property, offers secure storage, and supports predictions for potency and ADME properties. deepmirror is available in a single package with no hidden fees.

Schrödinger: Quantum Mechanics & Free Energy Calculations

Schrödinger's platform integrates advanced quantum chemical methods with machine learning approaches for molecular catalyst design and drug discovery.  Their flagship product, Live Design, provides an entry point into most of Schroedinger’s tools with scalable licensing. A collaboration with Google Cloud aims to substantially increase the speed and capacity of Schrödinger's physics-based molecule modeling platform, enabling the simulation of billions of potential compounds per week. Schrödinger has developed new scoring functions, including GlideScore, which is designed to maximize separation of compounds with strong binding affinity from those with little to no binding ability. They've also introduced DeepAutoQSAR, a machine learning solution for predicting molecular properties based on chemical structure. Schroedinger’s software comes in a modular licensing model in which different capabilities can be bolted onto the main software and tends to cost more than other solutions.

Chemaxon: Enterprise-Scale Chemical Intelligence

Chemaxon offers several software solutions for drug discovery, including the Plexus Suite and Design Hub. The Plexus Suite is a web-based software package that incorporates Chemaxon's chemistry capabilities for accessing, displaying, searching, and analyzing scientific data. It includes tools like Plexus Connect for data querying and visualization, Plexus Design for virtual library design, and Plexus Mining for chemically intelligent data mining. Design Hub is Chemaxon's platform for compound design and tracking in drug discovery. It connects scientific hypotheses, candidate compound selection, and computational capabilities. Chemaxon’s tools are mostly available as pay-per-use.

Optibrium: AI-Guided Lead Optimization

Optibrium's StarDrop software offers advanced capabilities for AI-guided lead optimization and provides a comprehensive platform for small molecule design, optimization, and data analysis. StarDrop uses patented rule induction and sensitivity analysis methods to develop optimization strategies. The software includes high-quality QSAR models for predicting ADME and physicochemical properties. Reaction-based library enumeration (RBE) allows chemists to easily generate new molecules using tractable chemical reactions. StarDrop connects with Optibrium's Cerella platform, leveraging advanced deep learning methods to extract additional value from compound data and BioPharmics for molecular docking. The software offers comprehensive data analysis, visualization, and design capabilities that can be integrated into existing informatics infrastructure. Similar to Schordinger’s software, Optibrium exhibits a modular pricing model with many extra features that can be added to the main software.

Cresset: Advanced Protein-Ligand Modeling

Cresset's Flare V8 offers advanced capabilities for protein-ligand modeling. It includes Free Energy Perturbation (FEP) enhancements that support more real-life drug discovery projects and ligands with different net charges. It also released Molecular Mechanics and Generalized Born Surface Area (MM/GBSA) method for calculating binding free energy of ligand-protein complexes, and Radius of Gyration (RG) plots to characterize protein size and study structural changes, flexibility, and dynamics over molecular dynamics trajectories. The overall workflow has enhanced protein and homology modeling features and streamlined workflows and customizable parameters. Cresset also provides Torx, a chemistry aware, web-based platform that supports hypothesis-driven drug design by centralizing all project data with dedicated, stand-alone modules to deliver a complete DMTA discovery solution.

DataWarrior: Open-Source Cheminformatics & Machine Learning

DataWarrior is an open-source program that offers chemical intelligence and data analysis capabilities for drug discovery. It can combine dynamic graphical views and interactive row filtering with chemical intelligence. It also supports various chemical descriptors encoding aspects of chemical structures, including chemical graphs, functionality, and 3D pharmacophore features. Additionally, it supports the development of QSAR models using molecular descriptors and machine learning techniques, and enables prediction of missing values using supervised machine learning methods and chosen molecular descriptors. It offers scatter plots, box plots, bar charts, and pie charts for visualizing numerical and categorical data.

Looking Ahead to 2025

The drug discovery software landscape of 2024 reflects remarkable technological advancement and specialization across the industry. While each platform offers unique advantages, the most successful solutions share fundamental characteristics: robust AI capabilities, seamless integration potential, and user-centric design. Scientists must carefully evaluate their specific research requirements and operational capabilities when selecting from this diverse range of options. Whether choosing comprehensive suites like Schrödinger and Chemaxon with flexible licensing for many different tools, specialized tools like Cresset, or broad and comprehensive AI-driven platforms like deepmirror, success lies in matching the solution to specific research objectives and organizational needs. As the field continues to evolve in 2025 and beyond, these platforms will undoubtedly play an increasingly crucial role in accelerating drug discovery and development processes.

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